Kiri Wagstaff : Research Bibliography
Automatically generated from a bibfile
If you notice any errors or broken links, please let me know:
wkiri@cs.cornell.edu


Topics:
General Artificial Intelligence General Machine Learning Clustering: Evaluation
Clustering: Incorporating Constraints Clustering: Methods Clustering: Applications
Clustering: Analysis Spectroscopy, Mars, Spectral Analysis, Remote Sensing Image Processing
Prior/background knowledge Weakly-supervised Learning Case based learning
Decision trees Machine Learning: Bias Machine Learning: Theory
Data Mining Model Selection General Natural Language Processing
NLP: Coreference Conference Proceedings Misc (unfiled)



General Artificial Intelligence

Poole, D. and Mackworth, A. and Goebel, R. Computational Intelligence: a logical approach. 1998. Oxford University Press, Oxford.


General Machine Learning

Abu-Mostafa, Y. S. Financial model calibration using consistency hints. 2001. IEEE Transactions on Neural Networks, 12(4):791-808.

Mjolsness, E. and DeCoste, D. Machine learning for science: State of the art and future prospects. 2001. Science, 293(5537):2051-2055.

DeCoste, D. and Wagstaff, K. Alpha seeding for support vector machines. 2000. in Proceedings of the International Conference on Knowledge Discovery and Data Mining, pages 345-349.

Jain, A. K. and Duin, R. and Mao, J. Statistical pattern recognition: a review. 2000. MSU-CSE-00-5, Department of Computer Science, Michigan State University.

Thornton, C. Truth from Trash: How Learning Makes Sense. 2000. The MIT Press.

Walczak, S. and Grimbergen, R. Pattern analysis and analogy in shogi: predicting shogi moves from prior experience. 2000. Knowledge and Information Systems, 2:185-200.

Mitchell, T. Machine Learning. 1997. McGraw-Hill.

Abu-Mostafa, Y. S. Hints. 1995. Neural Computation, 7:639-671.

Holte, R. Very simple classification rules perform well on most commonly used datasets. 1993. Machine Learning, 3:63-91.

Iba, W. and Gennari, J. H. Learning to recognize movements. 1991. in Concept Formation: Knowledge and Experience in Unsupervised Learning, pages 355-386. Morgan Kaufmann, San Mateo, CA.


Clustering: Evaluation

Carletta, J. Assessing agreement on classification tasks: the kappa statistic. 1996. Computational Linguistics, 22(2):249-254.

Siegel, S. and Castellan, Jr., N. J. Nonparametric Statistics for the Behavioral Sciences. 1988. McGraw-Hill. Second edition.

Milligan, G. W. and Cooper, M. C. An examination of procedures for determining the number of clusters in a data set. 1985. Psychometrika, 50:159-179.

Fowlkes, E. B. and Mallows, C. L. A method for comparing two hierarchical clusterings. 1983. Journal of the American Statistical Association, 78(383):553-569.

Rand, W. M. Objective criteria for the evaluation of clustering methods. 1971. Journal of the American Statistical Association, 66(366):846-850.


Clustering: Incorporating Constraints

Wagstaff, K. Intelligent Clustering with Instance-Level Constraints. 2002. Ph. D. thesis, Cornell University. (in preparation)

Basu, S. and Bannerjee, A. and Mooney, R. Semi-supervised Clustering by Seeding. 2002. in Proceedings of the Nineteenth International Conference on Machine Learning, pages (null).

Zhu, X. and Chu, T. and Zhu, J. and Caruana, R. Heuristically inducing a distance metric from user preferences for clustering. 2001. Unpublished. (Unpublished manuscript)

Cohn, D. and Caruana, R. and McCallum, A. Semi-supervised clustering with user feedback. 2000. Unpublished. (Unpublished manuscript)

Klein, D. and Kamvar, S. D. and Manning, C. D. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. 2002. in Proceedings of the Nineteenth International Conference on Machine Learning, pages (null).

Klein, D. and Kamvar, S. D. and Manning, C. D. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. 2002. 2002-11, Stanford University.

Epter. UpdateMe. 2001. Ph. D. thesis, RPI?.

Tung, A. K. H. and Ng, R. T. and Lakshmanan, L. V. S. and Han, J. Constraint-Based Clustering in Large Databases. 2001. in Proceedings of the 2001 International Conference on Database Theory, pages 405-419.

Wagstaff, K. and Cardie, C. and Rogers, S. and Schroedl, S. Constrained k-means clustering with background knowledge. 2001. in Proceedings of the Eighteenth International Conference on Machine Learning, pages 577-584. Morgan Kaufmann, Williamstown, MA.

Bradley, P. S. and Bennett, K. P. and Demiriz, A. Constrained k-means clustering. 2000. MSR-TR-2000-65, Microsoft Research, Redmond, WA.

Wagstaff, K. and Cardie, C. Clustering with instance-level constraints. 2000. in Proceedings of the Seventeenth International Conference on Machine Learning, pages 1103-1110. Morgan Kaufmann, Palo Alto, CA.

Demiriz, A. and Bennett, K. P. and Embrechts, M. J. Semi-supervised clustering using genetic algorithms. 1999. in Proceedings of Artificial Neural Networks in Engineering '99, pages (null).

Kleinberg, J. and Tardos, E. Approximation algorithms for classification problems with pairwise relationships: Metric labeling and Markov random fields. 1999. in Proceedings of the 40th IEEE Symposium on Foundations of Computer Science, pages 14-23.

Bachar, K. and Lerman, I. Statistical conditions for a linear complexity for an algorithm of hierarchical classification under constraint of contiguity. 1998. in Advances in Data Science and Classification, pages 131-136. Springer-Verlag.

Batagelj, V. and Ferligoj, A. Constrained clustering problems. 1998. in Advances in Data Science and Classification, pages 137-144. Springer-Verlag.

Beck, J. C. and Fox, M. S. A generic framework for constraint-directed search and scheduling. 1998. AI Magazine, 19(4):101-130.

Ambroise, C. and Dang, M. and Govaert, G. Clustering of spatial data by the EM algorithm. 1997. in geoENV I-Geostatistics for Environmental Applications, pages 493-504. Kluwer Academic Publishers.

Theiler, J. and Gisler, G. A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation. 1997. in Proceedings of SPIE (International Society for Optical Engineering), pages 108-118.

Gordon, A. D. A survey of constrained classification. 1996. Computational Statistics & Data Analysis, 21:17-29.

Roberts, S. and Gisler, G. and Theiler, J. Spatio-spectral image analysis using classical and neural algorithms. 1996. in Smart Engineering Systems: Neural Networks, Fuzzy Logic, and Evolutionary Programming, pages 425-430. ASME Press, New York, NY.

Murtagh, F. Unsupervised catalog classification. 1995. in Astronomical Data Analysis Software and Systems IV, pages 264-267.

Murtagh, F. The Kohonen self-organizing map method: an assessment. 1995. Journal of Classification, 12:165-190.

Murtagh, F. Contiguity-constrained hierarchical clustering. 1995. in Partitioning Data Sets (DIMACS Workshop), pages 35-54. American Mathematical Society.

Ellman, T. Synthesis of abstraction hierarchies for constraint satisfaction by clustering approximately equivalent objects. 1993. in Proceedings of the Tenth International Conference on Machine Learning, pages (null). University of Massachusetts, Amherst, MA.

Masson, P. and Pieczynski, W. SEM algorithm and unsupervised statistical segmentation of satellite images. 1993. IEEE Transactions on Geoscience and Remote Sensing, 31(3):618-633.

Pereira, F. and Schabes, Y. Inside-Outside reestimation from partially bracketed corpora. 1992. in Proceedings of the 30th Annual Meeting of the Association for Computational Linguistics, pages 128-135. Newark, DE.

Thompson, K. and Langley, P. Case studies in the use of background knowledge: incremental concept formation. 1992. in Proceedings of the AAAI-92 Workshop on Constraining Learning with Prior Knowledge, pages 60-68. The AAAI Press, San Mateo, CA.

De Raedt, L. and Bruynooghe, M. and Martens, B. Integrity constraints and interactive concept-learning. 1991. in Proceedings of the Eighth International Workshop on Machine Learning, pages 394-398. Morgan Kaufmann.

Jain, A. and Farrokhnia, F. Unsupevised texture segmentation using Gabor filters. 1991. Pattern Recognition, 24(12):1167-1186.

McKusick, K. B. and Langley, P. Constraints on tree structure in concept formation. 1991. in Proceedings of the Twelfth International Conference on Artificial Intelligence, pages 810-816. Morgan Kaufmann, Sydney, Australia.

Ripley, B. Statistical inference for spatial processes. 1991. Cambridge University Press.

Oliver, M. and Webster, R. A geostatistical basis for spatial weighting in multivariate classification. 1989. Mathematical Geology, 21:15-35.

Murtagh, F. A survey of algorithms for contiguity-constrained clustering and related problems. 1985. The Computer Journal, 28(1):82-88.

Ferligoj, A. and Batagelj, V. Some types of clustering with relational constraints. 1983. Psychometrika, 48(4):541-552.

Perruchet, C. Constrained agglomerative hierarchical classification. 1983. Pattern Recognition, 16(2):213-217.

Ferligoj, A. and Batagelj, V. Clustering with relational constraint. 1982. Psychometrika, 47(4):413-426.

Lefkovitch, L. P. Conditional clustering. 1980. Biometrics, 36:43-58.

Gordon, A. D. Classification in the presence of constraints. 1973. Biometrics, 29:821-827.

Berry, B. Essay on commodity flows and the spatial structure of the Indian economy. 1966. Research paper, 111, University of Chicago.


Clustering: Methods

Liu, B. and Xia, Y. and Yu, P. Clustering through decision tree construction. 2000. in Proceedings of the ACM International Conference on Information and Knowledge Management, pages (null). Washington, DC.

Zhang, B. and Hsu, M. and Dayal, U. K-harmonic means - a spatial clustering algorithm with boosting. 2000. in TSDM Workshop at PKDD 2000, pages (null).

Fasulo, D. An analysis of recent work on clustering algorithms. 1999. Unpublished.

Hoeppner, F. and Klawonn, F. and Kruse, R. and Runkler, T. Fuzzy Cluster Analysis. 1999. Wiley.

Krishna, K. and Murty, M. N. Genetic K-means Algorithm. 1999. IEEE Transactions on Systems, Man, and Cybernetics -- Part A: Systems and Humans, 29(3):433-439.

Pelleg, D. and Moore, A. Accelerating Exact k-means Algorithms with Geometric Reasoning. 1999. in Knowledge Discovery and Data Mining, pages 277-281.

Alsabti, K. and Ranka, S. and Singh, V. An efficient k-means clustering algorithm. 1998. in IPPS/SPDP Workshop on High Performance Data Mining, pages (null).

Blockeel, H. and De Raedt, L. and Ramon, J. Top-down induction of clustering trees. 1998. in Proceedings of the Fifteenth International Conference on Machine Learning, pages 55-63. Morgan Kaufmann, San Francisco, CA.

Boley, D. L. Principal direction divisive partitioning. 1998. Data Mining and Knowledge Discovery, 2(4):325-344.

Bradley, P. S. and Fayyad, U. M. Refining initial points for k-means clustering. 1998. in Proceedings of the Fifteenth International Conference on Machine Learning, pages 91-99. Morgan Kaufmann, San Francisco, CA.

Shmoys, D. B. and Tardos, E. and Aardal, K. Approximation algorithms for facility location. 1997. in Proceedings of the Twenty-ninth Annual ACM Symposium on the Theory of Computing, pages 265-274.

Fisher, D. Iterative optimization and simplification of hierarchical clusterings. 1996. Journal of Artificial Intelligence Research, 4:147-179.

Upal, M. A. and Neufeld, E. M. Comparison of unsupervised classifiers. 1996. in Proceedings of the First International Conference on Information, Statistics and Induction in Science, pages 342-353. World Scientific, Melbourne, Australia.

Bertrand, P. Structural properties of pyramidal clustering. 1995. in Partitioning Data Sets (DIMACS Workshop), pages 35-54. American Mathematical Society.

Hansen, P. and Jaumard, B. and Mladenovic, N. How to choose K entities among N. 1995. in Partitioning Data Sets (DIMACS Workshop), pages 105-116. American Mathematical Society.

Mucha, H. Clustering in an interactive way. 1995. Chapter in XploRe - An Interactive Statistical Computing Environment. Springer-Verlag.

Olson, C. Parallel algorithms for hierarchical clustering. 1995. Parallel Computing, 21:1313-1325.

Vandev, D. and Tsvetanova, Y. About ordering features of single linkage clustering algorithm. 1995. in Proceedings of SDA-95 and SDA-96, pages 99-107.

Fisher, D. and Xu, L. and Zard, N. Ordering effects in clustering. 1992. in Proceedings of the Ninth International Conference on Machine Learning, pages 163-168. Morgan Kaufmann, San Francisco, CA.

Anderson, J. R. and Matessa, M. An incremental bayesian algorithm. 1991. in Concept Formation: Knowledge and Experience in Unsupervised Learning, pages 45-70. Morgan Kaufmann, San Mateo, CA.

Krivanek, M. Algorithmic and Geometric Aspects of Cluster Analysis. 1991. Academia Nakladatelstvi Ceskoslovenske, Praha.

Thompson, K. and Langley, P. Concept formation in structured domains. 1991. in Concept Formation: Knowledge and Experience in Unsupervised Learning, pages 127-161. Morgan Kaufmann, San Mateo, CA.

Cheeseman, P. and Kelly, J. and Self, M. and Stutz, J. and Taylor, W. and Freeman, D. Autoclass: A Bayesian classification system. 1988. in Proceedings of the Fifth International Workshop on Machine Learning, pages 54-64. Morgan Kaufmann, Ann Arbor, MI.

Jain, A. K. and Dubes, R. C. Algorithms for Clustering Data. 1988. Prentice Hall.

Fisher, D. Knowledge acquisition via incremental conceptual clustering. 1987. Machine Learning, 2:139-172.

Gluck, M. A. and Corter, J. E. Information, uncertainty, and the utility of categories. 1985. in Proceedings of the Seventh Annual Conference of the Cognitive Science Society, pages 283-287. Lawrence Erlbaum, Hillsdale, NJ.

Gonzalez, T. Clustering to minimize the maximum intercluster distance. 1985. Theoretical Computer Science, 38:293-306.

Gray, R. M. Vector quantization. 1984. IEEE ASSP Magazine, 1:4-29.

Urquhart, R. Graph theoretical clustering based on limited neighbourhood sets. 1982. Pattern Recognition, 15(3):173-187.

Gordon, A. D. Classification. 1981. Chapman and Hall, New York, NY.

Dempster, A. P. and Laird, N. M and Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. 1977. Journal of the Royal Statistical Society, 39(1):1-38.

Hartigan, J. A. Clustering Algorithms. 1975. Wiley, New York, NY.

Anderberg, M. Cluster Analysis for Applications. 1973. Academic Press.

Sibson, R. SLINK: An optimally efficient algorithm for the single-link cluster method. 1973. The Computer Journal, 16(1):30-34.

Zahn, C. Graph-theoretical methods for detecting and describing Gestalt clusters. 1971. IEEE Transactions on Computing, C-20:68-86.

Lance, G. N. and Williams, W. T. A general theory of classificatory sorting strategies, 1. Hierarchical systems. 1967. The Computer Journal, 9:373-380.

MacQueen, J. B. Some methods for classification and analysis of multivariate observations. 1967. in Proceedings of the Fifth Symposium on Math, Statistics, and Probability, pages 281-297. University of California Press, Berkeley, CA.

Jancey, R. C. Multidimensional group analysis. 1966. Australian Journal of Botany, 14(1):127-130.

Forgy, E. W. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. 1965. Biometrics, 21:768-769.

Ward, Jr., J. H. Hierarchical groupings to optimise an objective function. 1963. Journal of the American Statistical Association, 58(301):236-244.


Clustering: Applications

Brown, D. E. and Gunderson, L. F. Using clustering to discover the preferences of computer criminals. 2001. IEEE Transactions on Systems, Man, and Cybernetics -- Part A: Systems and Humans, 31(4):311-318.

Schroedl, S. and Rogers, S. and and Wilson, C. Map Refinement from GPS Traces. 2000. RTC Report No. 6, DaimlerChrysler Research and Technology North America.

Schroedl, S. and Wagstaff, K. and Rogers, S. and Langley, P. and Wilson, C. Mining GPS traces for map refinement. 2001. (in preparation), (null):(null).

Bertin, E. Mining pixels: The extraction and classification of astronomical sources. 2000. in Mining the Sky: Proceedings of the MPA/ESO/MPE Workshop, pages 353-371. European Southern Observatory. Springer.

Djorgovski, S. G. and Brunner, R. J. and Mahabal, A. A. and Odewahn, S. C. and de Carvalho, R. R. and Gal, R. R. and Stolorz, P. and Granat, R. and Curkendall, D. and Jacaob, J. and Castro, S. Exploration of Large Digital Sky Surveys. 2000. in Mining the Sky: Proceedings of the MPA/ESO/MPE Workshop, pages 305-322. European Southern Observatory. Springer.

Bellot, P. and El-Beze, M. A clustering method for information retrieval. 1999. IR-0199, Laboratoire d'Informatique d'Avignon, France.

Cardie, C. and Wagstaff, K. Noun phrase coreference as clustering. 1999. in Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pages 82-89. Association for Computational Linguistics, University of Maryland, MD.

King, I. and Lau, T. K. Non-hierarchical clustering with rival penalized competitive learning for information retrieval. 1999. in Proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition, pages (null).

Rogers, S. and Langley, P. and Wilson, C. Mining GPS data to augment road models. 1999. in Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pages 104-113. ACM Press, San Diego, CA.

Soh, L. and Tsatsoulis, C. Segmentation of satellite imagery of natural scenes using data mining. 1999. IEEE Transactions on Geoscience and Remote Sensing, 37(2):1086-1099.

Yona, G. and Linial, N. and Linial, M. ProtoMap: automatic classification of protein sequences, a hierarchy of protein families. 1999. in Proteins: Structure, Function, and Genetics, pages 360-378.

Zhuang, Y. and Rui, Y. and Huang, T. and Mehrotra, S. Adaptive key frame extraction using unsupervised clustering. 1998. in Proceedings of the IEEE International Conference on Image Processing, pages 866-870. Chicago, IL.

King, I. and Lau, T. K. Competitive learning clustering for information retrieval in image databases. 1997. in Proceedings of the 1997 International Conference on Neural Information Processing, pages 906-909.

Megiddo, N. and Sarkar, V. Optimal weighted loop fusion for parallel programs. 1997. in Proceedings of the Ninth Annual ACM Symposium on Parallel Algorithms and Architectures, pages 282-291.

Singhai, S. and McKinley, K. A Parameterized Loop Fusion Algorithm for Improving Parallelism and Cache Locality. 1997. The Computer Journal, 40(6):340-355.

Xiang, Z. Color image quantization by minimizing the maximum intercluster distance. 1997. ACM Transactions on Graphics, 16(3):260-276.

Merenyi, E. and Singer, R. B. and Miller, J. S. Mapping of spectral variations on the surface of Mars from high spectral resolution telescopic images. 1996. Icarus, 124:280-295.

Yoo, J. and Gray, A. and Roden, J. and Fayyad, U. M. and de Carvalho, R. R. and Djorgovski, S. G. Analysis of Digital POSS-II Catalogs using hierarchical unsupervised learning algorithms. 1996. in Astronomical Data Analysis Software and Systems V, pages 41-44.

de Carvalho, R. R. and Djorgovski, S. G. and Weir, N. and Fayyad, U. and Cherkauer, K. and Roden, J. and Gray, A. Clustering analysis algorithms and their applications to digital POSS-II catalogs. 1995. in Astronomical Data Analysis Software and Systems IV, pages 272-275.

Burl, M. C. and Fayyad, U. and Smyth, P. and Perona, P. and Burl, M. P. Automating the hunt for volcanoes on Venus. 1994. in Proceedings of the 1994 Conference on Computer Vision and Pattern Recognition, pages (null).

Fontaine, V. and Leich, H. and Hennebert, J. Influence of vector quantization on isolated word recognition. 1994. in Signal Processing VII, Theories and Applications. Proceedings of EUSIPCO-94. Seventh European Signal Processing Conference, pages 115-118. Eur. Assoc. Signal Process, Lausanne, Switzerland.

Kennedy, K. and McKinley, K. S. Maximizing loop parallelism and improving data locality via loop fusion and distribution. 1994. CRPC-TR94370, Center for Research on Parallel Computation.

Marroquin, J. and Girosi, F. Some extensions of the k-means algorithm for image segmentation and pattern recognition. 1993. AI Memo, 1390, Massachusetts Institute of Technology, Cambridge, MA.

Cheeseman, P. and Stutz, J. and Self, M. and Taylor, W. and Geobel, J. and Volk, K. and Walker, H. Automatic classification of spectra from the Infrared Astronomical Satellite (IRAS). 1989. NASA Reference Publication, 1217, National Technical Information Service, Springfield, VA.

Goebel, J. and Volk, K. and Walker, H. and Gerbault, F. and Cheeseman, P. and Self, M. and Stutz, J. and Taylor, W. A Bayesian classification of the IRAS LRS Atlas. 1989. Astronomy and Astrophysics, 222:L5-L8.

Wharton, S. W. A generalized histogram clustering scheme for multidimensional image data. 1983. Pattern Recognition, 16(2):193-199.

Narendra, P. M. and Goldberg, M. A non-parametric clustering cheme for Landsat. 1977. Pattern Recognition, 9:207-215.


Clustering: Analysis

Neal, R. and Hinton, G. A view of the EM algorithm that justifies incremental, sparse and other variants. 1998. in Learning in Graphical Models, pages 355-368. Kluwer Academic Publishers.

Selman, B. and Mitchell, D. G. and Levesque, H. J. Generating hard satisfiability problems. 1996. Artificial Intelligence, 81:17-29.

Bottou, L. and Bengio, Y. Convergence properties of the k-means algorithm. 1995. Advances in Neural Information Processing Systems, 7:585-592.

Langley, P. Order effects in incremental learning. 1995. in Learning in humans and machines: Towards an interdisciplinary learning science, pages (null). Oxford: Elsevier.

Selim, S. Z. and Ismail, M. A. K-means-type algorithms: A generalized convergence theorem and characterization of local optimality. 1984. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(1):81-87.

Garey, M. R. and Johnson, D. S. Computers and Intractability: A Guide to the Theory of NP-Completeness. 1979. W. H. Freeman and Company.


Spectroscopy, Mars, Spectral Analysis, Remote Sensing

Bell, J. F. and McSween Jr., H. Y. and Crisp, J. A. and Morris, R. V. and Murchie, S. L. and Bridges, N. T. and Johnson, J. R. and Britt, D. T. and Golombek, M. P. and Moore, H. J. and Ghosh, A. and Bishop, J. L. and Anderson, R. C. and Bruckner, J. and Economou, T. and Greenwood, J. P. and Gunnlaugsson, H. P. and Hargraves, R. M. and Hviid, S. and Knudsen, J. M. and Madsen, M. B. and Reid, R. and Rieder, R. and Soderblom, L. Mineralogic and compositional properties of Martian soil and dust: Results from Mars Pathfinder. 2000. Journal of Geophysical Research, 105(E1):1721-1755.

McSween Jr., H. Y. and Murchie, S. L. and Crisp, J. A. and Bridges, N. T. and Anderson, R. C. and Bell, J. F. and Britt, D. T. and Bruckner, J. and Dreibus, G. and Economou, T. and Ghosh, A. and Golombek, M. P. and Greenwood, J. P. and Johnson, J. R. and Moore, H. J. and Morris, R. V. and Parker, T. J. and and Rieder, R. and Singer, R. and Wanke, H. Chemical, multispectral, and textural constraints on the composition and origin of rocks at the Mars Pathfinder landing site. 1999. Journal of Geophysical Research, 104(E4):8679-8715.

Bell, J. F. and Wolff, M. J. and James, P. B. and Clancy, R. T. and Lee, S. W. and Martin, L. J. Mars surface mineralogy from Hubble Space Telescope imaging during 1994-1995: Observations, calibration, and initial results. 1997. Journal of Geophysical Research, 102(E4):9109-9123.

Bell, J. F. Visible and Near-Infrared spectroscopy. 1997. in Encyclopedia of Planetary Sciences, pages 911-915. Chapman and Hall.

Pieters, C. M. and Mustard, J. F. and Sunshine, J. M. Quantitative mineral analyses of planetary surfaces using reflectance spectroscopy. 1996. in Mineral Spectroscopy: A Tribute to Roger G. Burns, pages 307-325. The Geochemical Society.

Gulati, R. K. and Gupta, R. and Gothoskar, P. and Khobragade, S. Automated classification of a large database of stellar spectra. 1995. in Astronomical Data Analysis Software and Systems IV, pages 253-256.

Bell, J. F. Charge-coupled device imaging spectroscopy of Mars. 1992. Icarus, 100:575-597.

Lillesand, T. M. and Kiefer, R. W. 1987. Chapter in Remote Sensing and Image Interpretation. Wiley.

Morris, R. V. and Lauer Jr., H. V. and Lawson, C. A. and Gibson Jr., E. K. and Nace, G. A. and Stewart, C. Spectral and other physicochemical properties of submicron powders of hematite (\alpha-Fe$_2{O_3), maghemite (ma-Fe_2{O_3), magnetite (Fe_3{O_4), goethite (pa-FeOOH), and lepidocrocite (ma-FeOOH). 1985. Journal of Geophysical Research, 90:3126-3144.

Hunt, G. R. Electromagnetic radiation: The communication link in remote sensing. 1980. in Remote Sensing in Geology, pages 5-45. Wiley, New York, NY.

Adams, J. B. Visible and near-infrared diffuse reflectance spectra of pyroxenes as applied to remote sensing of solid objects in the solar system. 1974. Journal of Geophysical Research, 79:4829-4836.

McCord, T. B. and Elias, J. H. and Westphal, J. A. Mars: The spectral albedo (0.3-2.5\mu) of small bright and dark regions. 1971. Icarus, 14:245-251.


Image Processing

Montolio, P. and Gasull, A. and Monte, E. and Torres, L. and Marques, F. Analysis and optimization of the k-means algorithm for remote sensing applications. 1992. in Pattern Recognition and Image Analysis, pages 155-170. World Scientific, Singapore.

Schowengerdt, R. A. Techniques for Image Processing and Classification in Remote Sensing. 1983. Academic Press.

Adams, J. B. and McCord, T. B. Mars: Interpretation of spectral reflectivity of light and dark regions. 1969. Journal of Geophysical Research, 74:4851-4856.

Kinderman, R. and Snell, J. L. Markov Random Fields and Their Applications. 1980. American Math Society, Providence, RI.


Prior/background knowledge

Talavera, L. and Bejar, J. Integrating declarative knowledge in hierarchical clustering tasks. 1999. in Proceedings of the International Symposium on Intelligent Data Analysis, pages 211-222. Springer-Verlag, Amsterdam, The Netherlands.

Bejar, J. and Cortes, U. Experiments with domain knowledge in unsupervised learning: using and revising theories. 1998. Computacion y Sistemas, 1(3):136-143.

Greiner, R. and Grove, A. and Schuurmans, D. On learning hierarchical classifications. 1997. Unpublished.

Lampinen, J. and Selonen, A. Using background knowledge in multilayer perceptron learning. 1997. in Proceedings of the Tenth Scandinavian Conference on Image Analysis, pages 545-549.

Feldman, R. and Hirsh, H. Mining associations in text in the presence of background knowledge. 1996. in Proceedings of the Second International Conference on Knowledge Discovery from Databases, pages (null).

Janikow, C. A methodology for processing problem constraints in genetic programming. 1996. Computers and Mathematics with Applications, 32(8):97-113.

Hirsh, H. and Noordewier, M. Using background knowledge to improve inductive learning of DNA sequences. 1994. in Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Applications, pages 351-357. IEEE Computer Society Press.


Weakly-supervised Learning

Riloff, E. and Jones, R. Learning dictionaries for information extraction by multi-level bootstrapping. 1999. in Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 474-479. AAAI Press / MIT Press, Orlando, FL.

Thompson, C. A. and Califf, M. A. and Mooney, R. J. Active learning for natural language parsing and information extraction. 1999. in Proceedings of the Sixteenth International Conference on Machine Learning, pages 406-414. Morgan Kaufmann, Bled, Slovenia.

Blum, A. and Mitchell, T. Combining labeled and unlabeled data with co-training. 1998. in Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages 92-100.

Nigam, K. and McCallum, A. and Thrun, S. and Mitchell, T. Learning to classify from labeled and unlabeled documents. 1998. in Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 792-799. The AAAI Press, Madison, WI.

Yarowsky, D. Unsupervised word sense disambiguation rivaling supervised methods. 1995. in Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pages 189-196. Cambridge, MA.

Cohn, D. and Atlas, L. and Ladner, R. Improving generalization with active learning. 1994. Machine Learning, 15(2):201-221.


Case based learning

Cardie, C. and Howe, N. Improving minority class prediction using case-specific feature weights. 1997. in Proceedings of the Fourteenth International Conference on Machine Learning, pages 57-65. Morgan Kaufmann, Vanderbilt University, Memphis, TN.


Decision trees

McCarthy, J. and Lehnert, W. Using decision trees for coreference resolution. 1995. in Proceedings of the Fourteenth International Conference on Artificial Intelligence, pages 1050-1055.

Quinlan, J. R. C4.5: Programs for Machine Learning. 1993. Morgan Kaufmann, San Mateo, CA.

Quinlan, J. R. Induction of decision trees. 1986. Machine Learning, 1(1):81-106.


Machine Learning: Bias

Gordon, D. and desJardins, M. Evaluation and selection of biases in machine learning. 1995. Machine Learning, 20(1/2):5-22.

desJardins, M. Evaluation of learning biases using probabilistic domain knowledge. 1994. in Computational Learning Theory and Natural Learning Systems, pages 95-112. The MIT Press.

Schaffer, C. Overfitting avoidance as bias. 1993. Machine Learning, 10(2):153-178.

Spears, W. and Gordon, D. Adaptive strategy selection for concept learning. 1991. in Proceedings of the First International Workshop on Multistrategy Learning, pages 231-246. Harpers Ferry, WV.

Utgoff, P. Machine Learning of Inductive Bias. 1986. Kluwer Academic Publishers.


Machine Learning: Theory

Wolpert, D. H. and MacReady, W. G. No free lunch theorems for optimization. 1997. IEEE Transactions on Evolutionary Computation, (null):47-94.

Schaffer, C. A conservation law for generalization performance. 1994. in Proceedings of the Eleventh International Conference on Machine Learning, pages 259-265. Morgan Kaufmann.

Blumer, A. and Ehrenfeucht, A. and Haussler, D. and Warmuth, M. K. Occam's razor. 1990. in Readings in Machine Learning, pages 201-204. Morgan Kaufmann.

Rissanen, J. Minimum description length principle. 1985. in Encyclopedia of Statistical Sciences, 5, pages 523-527. Wiley, New York.


Data Mining

Guha, S. and Rastogi, R. and Shim, K. CURE: An efficient algorithm for clustering large databases. 1998. in Proceedings of the ACM-SIGMOD International Conference on Management of Data, pages 73-84. Seattle, WA.

Zhang, T. and Ramakrishnan, R. and Livny, M. BIRCH: An efficient data clustering method for very large databases. 1996. in Proceedings of the ACM-SIGMOD International Conference on Management of Data, pages (null).

Agrawal, R. and Imielinski, T. and Swami, A. Mining associations between sets of items in massive databases. 1993. in Proceedings of the ACM-SIGMOD 1993 International Conference on Management of Data, pages 207-216. Washington, D.C..


Model Selection

Vaithyanathan, S. and Dom, B. Model selection in unsupervised learning with applications to document clustering. 1999. in Proceedings of the Sixteenth International Conference on Machine Learning, pages 433-443. Morgan Kaufmann, Bled, Slovenia.

Fraley, C. and Raftery, A. E. How many clusters? Which clustering method? Answers via model-based cluster analysis. 1998. Technical Report No. 329, University of Washington.

Brodley, C. E. Recursive automatic algorithm selection for inductive learning. 1994. Ph. D. thesis, University of Massachusetts, Amherst, MA.

Aha, D. W. Generalizing from case studies: a case study. 1992. in Proceedings of the Ninth International Conference on Machine Learning, pages 1-10. Morgan Kaufmann, University of Aberdeen, UK.


General Natural Language Processing

Strub, M. and Wolters, M. A probabilistic genre-independent model of pronominalization. 2000. in Proceedings of the North American Association of Computational Linguistics, pages 18-25.

Rooth, M. and Riezler, S. and Prescher, D. and Carroll, G. and Beil, F. Inducing a Semantically Annotated Lexicon via EM-Based Clustering. 1999. in Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pages 104-111. Association for Computational Linguistics.

Cardie, C. and Pierce, D. Error-driven pruning of treebank grammars for base noun phrase identification. 1998. in Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and COLING-98, pages 218-224. Association for Computational Linguistics, University of Montreal, Montreal, Canada.

Fellbaum, C. WordNet: An Electronical Lexical Database. 1998. The MIT Press, Cambridge, MA.

Pereira, F. and Tishby, N. and Lee, L. Distributional Clustering of English Words. 1993. in Proceedings of the 31th Annual Meeting of the Association for Computational Linguistics, pages 183-190. Association for Computational Linguistics, Ohio State University, Columbus, Ohio.

Cardie, C. A Case-Based Approach to Knowledge Acquisition for Domain-Specific Sentence Analysis. 1993. in Proceedings of the Eleventh National Conference on Artificial Intelligence, pages 798-803. AAAI Press / MIT Press, Washington, DC.


NLP: Coreference

Ng, V. and Cardie, C. Combining sample selection and error-driven pruning for machine learning of coreference rules. 2002. in Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, pages (null).

Soon, W. M. and Ng, H. T. and Lim, D. C. Y. A machine learning approach to coreference resolution of noun phrases. 2001. Computational Linguistics, 27(4):521-544.

Trouilleux, F. and Gaussier, E. and Bes, G. G. and Zaenen, A. Coreference resolution evaluation based on descriptive specificity. 2000. in Proceedings of the LREC 2000 Workshop on Linguistic Coreference, pages (null).

Mitkov, R. Anaphora resolution: the state of the art. 1999. Unpublished. (Working paper (Based on the COLING'98/ACL'98 tutorial on anaphora resolution))

Azzam, S. and Humphreys, K. and Gaizauskas, R. Evaluating a focus-based approach to anaphora resolution. 1998. in Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and COLING-98, pages 74-78. Association for Computational Linguistics.

Bagga, A. and Baldwin, B. Algorithms for scoring coreference chains. 1998. in Proceedings of the LREC 1998 Workshop on Linguistic Coreference, pages 563-566.

Ge, N. and Hale, J. and Charniak, E. A statistical approach to anaphora resolution. 1998. in Proceedings of the Sixth Workshop on Very Large Corpora, pages 161-170. ACL SIGDAT, Montreal, Canada.

Popescu-Belis, A. and Robba, I. Three new methods for evaluating reference resolution. 1998. in Proceedings of the LREC 1998 Workshop on Linguistic Coreference, pages (null).

Aone, Chinatsu and Bennett, William. Evaluating Automated and Manual Acquisition of Anaphora Resolution Strategies. 1995. in Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pages 122-129. Association for Computational Linguistics.

Appelt, D. and Hobbs, J. and Bear, J. and Israel, D. and Kameyama, M. and Kehler, A. and Martin, D. and Meyers, K. and Tyson, M. SRI International FASTUS system: MUC-6 test results and analysis. 1995. in Proceedings of the Sixth Message Understanding Conference, pages 237-248. Morgan Kaufmann, San Francisco, CA.

McCarthy, J. and Lehnert, W. Using Decision Trees for Coreference Resolution. 1995. in Proceedings of the Fourteenth International Conference on Artificial Intelligence, pages 1050-1055.

Vilain, M. and Burger, J. and Aberdeen, J. and Connolly, D. and Hirschman, L. A model-theoretic coreference scoring scheme. 1995. in Proceedings of the Sixth Message Understanding Conference, pages 45-52. Morgan Kaufmann, San Francisco, CA.

Dagan, I. and Itai, A. A Statistical Filter for Resolving Pronoun References. 1991. in Artificial Intelligence and Computer Vision, pages 125-135. Elsevier Science Publishers, North Holland.


Conference Proceedings

EMNLP/VLC-99. Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. 1999. Association for Computational Linguistics, University of Maryland, College Park, MD.

ULNLP-99. Unsupervised Learning in Natural Language Processing. 1999. Association for Computational Linguistics, University of Maryland, College Park, MD.

MUC-7. Proceedings of the Seventh Message Understanding Conference. 1998. Morgan Kaufmann, San Francisco, CA.

MUC-6. Proceedings of the Sixth Message Understanding Conference. 1995. Morgan Kaufmann, San Francisco, CA.

AAAI-92 Workshop. Proceedings of the AAAI-92 Workshop on Constraining Learning with Prior Knowledge. 1992. The AAAI Press, San Mateo, CA.


Misc (unfiled)

Andrade, M. A. and O'Donoghue, S. I. and Rost, B. Adaptation of protein surfaces to subcellular location. 1998. Journal of Molecular Biology, 276:517-525.

Navigation Technologies. Software Developer's Toolkit. 1996. Navigation Technologies, Inc., Sunnyvale, CA. 5.7.4 Solaris.

Bairoch, A. and Boeckman, B. The SWISS-PROT protein sequence data bank. 1992. Nucleic Acids Research, 20:2019-2022.

Cormen, T. and Leiserson, C. and Rivest, R. Introduction to Algorithms. 1990. The MIT Press, Cambridge, MA.


Kiri Wagstaff< Email: wkiri@cs.cornell.edu >