Machine Learning for the Detection of Oil Spills in Satellite Radar Images (original) (raw)
References
Aha, D., Kibler, D., & Albert, M. (1991). Instance-Based Learning Algorithms. Machine Learning, 6(1), 37-66. Google Scholar
Aha, D. (1992). Generalizing from Case Studies: ACase Study. Proceedings of the Ninth International Conference on Machine Learning (pp. 1-10), Morgan Kaufmann.
Brodley, C., & Smyth, P. (1995). The Process of Applying Machine Learning Algorithms. Working Notes for Applying Machine Learning in Practice: A Workshop at the Twelfth International Conference on Machine Learning, Technical Report AIC-95-023 (pp. 7-13), NRL, Navy Center for Applied Research in AI, Washington, DC. Google Scholar
Burl, M.C., Asker, L., Smyth, P., Fayyad, U.M., Perona, P., Crumpler, L., & Aubele, J. (this issue). Learning to Recognize Volcanoes on Venus. Machine Learning, 30, 165-194.
Caruana, R. (1993). Multitask Learning: A Knowledge-based Source of Inductive Bias. Proceedings of the Tenth International Conference on Machine Learning (pp. 41-48), Morgan Kaufmann.
Catlett, J. (1991). Megainduction: A Test Flight. Proceedings of the Eighth International Workshop on Machine Learning (pp. 596-599), Morgan Kaufmann.
Cherkauer, K.J., & Shavlik, J.W. (1994). Selecting Salient Features for Machine Learning from Large Candidate Pools through Parallel Decision-Tree Construction. In Kitano, H. & Hendler, J. (Eds.), Massively Parallel Artificial Intelligence (pp. 102-136), AAAI Press/MIT Press.
Clark, P. & Matwin, S. (1993). Using Qualitative Models to Guide Inductive Learning. Proceedings of the Tenth International Conference on Machine Learning (pp. 49-56), Morgan Kaufmann.
Clearwater, S., & Stern, E. (1991). A Rule-Learning Program in High Energy Physics Event Classification. Comp. Physics Comm., 67, 159-182. Google Scholar
DeRouin, E., Brown, J., Beck, H., Fausett, L., & Schneider, M. (1991). Neural Network Training on Unequally Represented Classes. In Dagli, C.H., Kumara, S.R.T., & Shin, Y.C. (Eds.), Intelligent Engineering Systems Through Artificial Neural Networks, pp. 135-145, ASME Press.
Dietterich, T.G., Hild, H., & Bakiri, G. (1995). A Comparison of ID3 and Backpropagation for English Text-to-Speech Mapping. Machine Learning, 18, 51-80. Google Scholar
Dietterich, T.G., Lathrop, R.H., & Lozano-Perez, T. (1997). Solving the Multiple-Instance Problem with Axis-Parallel Rectangles. Artificial Intelligence, 89(1-2), 31-71. Google Scholar
Ezawa, K.J., Singh, M., & Norton, S.W. (1996). Learning Goal Oriented Bayesian Networks for Telecommunications Management. Proceedings of the Thirteenth International Conference on Machine Learning (pp. 139-147), Morgan Kaufmann.
Fawcett, T., & Provost, F. (1997). Adaptive Fraud Detection. Data Mining and Knowledge Discovery, 1(3), 291-316. Google Scholar
Fayyad, U.M., Weir, N., & Djorgovski, S. (1993). SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys. Proceedings of the Tenth International Conference on Machine Learning (pp. 112-119), Morgan Kaufmann.
Floyd, S., & Warmuth, M. (1995). Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension. Machine Learning, 21, 269-304. Google Scholar
Hart, P.E. (1968). The Condensed Nearest Neighbor Rule. IEEE Transactions on Information Theory, IT-14, 515-516. Google Scholar
Haverkamp, D., Tsatsoulis, C., & Gogineni, S. (1994). The Combination of Algorithmic and Heuristic Methods for the Classification of Sea Ice Imagery. Remote Sensing Reviews, 9, 135-159. Google Scholar
Heerman, P. D., & Khazenie, N. (1992). Classification of Multispectral Remote Sensing Data using a backpropagation Neural Network. IEEE Trans. of Geoscience and Remote Sensing, 30, 81-88. Google Scholar
Holte, R. C., Acker, L., & Porter, B.W. (1989). Concept Learning and the Problem of Small Disjuncts. Proceedings of the International Joint Conference on Artificial Intelligence (pp. 813-818), Morgan Kaufmann.
Honda, T., Motizuki, H., Ho, T.B., & Okumura, M. (1997). Generating Decision Trees from an Unbalanced Data Set. In van Someren, M., & Widmer, G. (Eds.), Poster papers presented at the 9th European Conference on Machine Learning (pp. 68-77).
Hovland, H. A., Johannessen, J. A., & Digranes, G. (1994). Slick Detection in SAT Images. Proceedings of IGARSS'94 (pp. 2038-2040).
Ioerger, T.R., Rendell, L.A., & Subramaniam, S. (1995). Searching for Representations to Improve Protein Sequence Fold-Class Prediction. Machine Learning, 21, 151-176. Google Scholar
Keeney, R.L., & Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Tradeoffs, Cambridge University Press.
Kohavi, R., & John, G.H. (to appear). Wrappers for Feature Subset Selection. Artificial Intelligence (special issue on relevance).
Kohavi, R., & John, G.H. (1995). Automatic Parameter Selection by Minimizing Estimated Error. Proceedings of the Twelfth International Conference on Machine Learning (pp. 304-312), Morgan Kaufmann.
Kononenko, I., & Bratko, I. (1991). Information-Based Evaluation Criterion for Classifier' Performance. Machine Learning, 6, 67-80. Google Scholar
Kubat, M., Holte, R., & Matwin, S. (1997). Learning when Negative Examples Abound. Machine Learning: ECML-97, Lecture Notes in Artificial Intelligence 1224 (pp. 146-153), Springer.
Kubat, M., & Matwin, S. (1997). Addressing the Curse of Imbalanced Training Sets: One-Sided Sampling. Proceedings of the Fourteenth International Conference on Machine Learning (pp. 179-186), Morgan Kaufmann.
Kubat, M., Pfurtscheller, G., & Flotzinger D. (1994). AI-Based Approach to Automatic Sleep Classification. Biological Cybernetics, 79, 443-448. Google Scholar
Kubat, M., & Widmer, G. (Eds.) (1996). Proceedings of the ICML'96 Pre-Conference Workshop on Learning in Context-Sensitive Domains.
Langley, P., & Simon, H.A. (1995). Applications of Machine Learning and Rule Induction. Communications of the ACM, 38(11), 55-64. Google Scholar
Lewis, D., & Catlett, J. (1994). Heterogeneous Uncertainty Sampling for Supervised Learning. Proceedings of the Eleventh International Conference on Machine Learning (pp. 148-156), Morgan Kaufmann.
Lewis, D., & Gale, W. (1994). A Sequential Algorithm for Training Text Classifiers. Proceedings of the Seventeenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 3-12), Springer-Verlag.
Lubinsky, David (1994). Bivariate Splits and Consistent Split Criteria in Dichotomous Classification Trees. Ph.D. thesis, Computer Science, Rutgers University.
Murphy, P., & Aha, D. (1994). UCIRepository of Machine Learning Databases (machine-readable data repository). University of California, Irvine. Google Scholar
Ossen, A., Zamzow, T., Oswald, H., & Fleck, E. (1994). Segmentation of Medical Images Using Neural-Network Classifiers. Proceedings of the International Conference on Neural Networks and Expert Systems in Medicine and Healthcare (NNESMED'94) (pp. 427-432).
Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., & Brunk, C. (1994). Reducing Misclassification Costs. Proceedings of the Eleventh International Conference on Machine Learning (pp. 217-225), Morgan Kaufmann.
Pfurtscheller, G., Flotzinger, D., & Kalcher, J. (1992). Brain-Computer Interface-A New Communication Device for Handicapped Persons. In Zagler, W. (Ed.), Computer for Handicapped Persons: Proceedings of the Third International Conference (pp. 409-415).
Provost, F.J., & Buchanan, B.G. (1995). Inductive Policy: The Pragmatics of Bias Selection. Machine Learning, 20(1/2), 35-62. Google Scholar
Provost, F.J., & Fawcett, T. (1997). Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (pp. 43-48).
Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.
Riddle, P., Segal, R., & Etzioni, O. (1994). Representation Design and Brute-Force Induction in a Boeing Manufacturing Domain. Applied Artificial Intelligence, 8, 125-147. Google Scholar
Rieger, A. (1995). Data Preparation for Inductive Learning in Robotics. Proceedings of the IJCAI-95 workshop on Data Engineering for Inductive Learning (pp. 70-78).
Saitta, L., Giordana, A., & Neri, F. (1995). What Is the “Real World”?. Working Notes for Applying Machine Learning in Practice: A Workshop at the Twelfth International Conference on Machine Learning, Technical Report AIC-95-023 (pp. 34-40), NRL, Navy Center for Applied Research in AI, Washington, DC. Google Scholar
Shapiro, A.D. (1987). Structured Induction in Expert Systems. Addison-Wesley.
Skalak, D. (1994). Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms. Proceedings of the Eleventh International Conference on Machine Learning (pp. 293-301), Morgan Kaufmann.
Solberg, A.H.S., & Solberg, R. (1996). A Large-Scale Evaluation of Features for Automatic Detection of Oil Spills in ERS SAR Images. IEEE Symp. Geosc. Rem. Sens (IGARSS) (pp. 1484-1486).
Solberg, A.H.S., & Volden, E. (1997). Incorporation of Prior Knowledge in Automatic Classification of Oil Spills in ERS SAR Images. IEEE Symp. Geosc. Rem. Sens (IGARSS) (pp. 157-159).
Swets, J.A. (1988). Measuring the Accuracy of Diagnostic Systems. Science, 240, 1285-1293. Google Scholar
Tomek, I. (1976). Two Modifications of CNN. IEEE Transactions on Systems, Man and Cybernetics, SMC-6, 769-772. Google Scholar
Turney, P. (1995). Data Engineering for the Analysis of Semiconductor Manufacturing Data. Proceedings of the IJCAI-95 workshop on Data Engineering for Inductive Learning (pp. 50-59).
Turney, P. (1993). Exploiting Context when Learning to Classify. Proceedings of the European Conference on Machine Learning (pp. 402-407), Springer-Verlag.
van Rijsbergen, C.J. (1979). Information Retrieval (second edition), Butterworths.
Venables, W.N., & Ripley, B.D. (1994). Modern Applied Statistics with S-Plus. Springer-Verlag.
von Winterfeldt, D., & Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge University Press.
Widmer, G., & Kubat, M. (1996). Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning, 23, 69-101. Google Scholar
Zhang, J. (1992). Selecting Typical Instances in Instance-Based Learning. Proceedings of the Ninth International Machine Learning Workshop (pp. 470-479), Morgan Kaufmann.