Smyth, Padhraic

Combined from CaltechAUTHORS

• Chudova, Darya and Hart, Christopher, el al. (2004) Gene Expression Clustering with Functional Mixture Models; ISBN 0-262-20152-6; Advances in Neural Information Processing Systems 16 (NIPS 2003); 683-690
• Burl, Michael C. and Asker, Lars, el al. (1998) Learning to Recognize Volcanoes on Venus; Machine Learning; Vol. 30; No. 2-3; 165-194; 10.1023/A:1007400206189
• Smyth, Padhraic and Heckerman, David, el al. (1997) Probabilistic independence networks for hidden Markov probability models; Neural Computation; Vol. 9; No. 2; 227-269; 10.1162/neco.1997.9.2.227
• Fayyad, Usama M. and Smyth, Padhraic, el al. (1995) Automated analysis and exploration of image databases: Results, progress, and challenges; Journal of Intelligent Information Systems; Vol. 4; No. 1; 7-25; 10.1007/bf00962819
• Smyth, Padhraic and Fayyad, Usama, el al. (1995) Inferring Ground Truth from Subjective Labelling of Venus Images; ISBN 0-262-20104-6; Advances in Neural Information Processing Systems 7; 1085-1092
• Burl, M. C. and Fayyad, Usama M., el al. (1994) Automated analysis of radar imagery of Venus: handling lack of ground truth; ISBN 0-8186-6952-7; 1994 IEEE International Conference on Image Processing, Proceedings (ICIP-94); 236-240; 10.1109/ICIP.1994.413852
• Burl, M. C. and Fayyad, U. M., el al. (1994) Automating the Hunt for Volcanoes on Venus; ISBN 0-8186-5825-8; Proceedings 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 302-309; 10.1109/CVPR.1994.323844
• Zeng, Zheng and Goodman, Rodney M., el al. (1994) Discrete recurrent neural networks for grammatical inference; IEEE Transactions on Neural Networks; Vol. 5; No. 2; 320-330; 10.1109/72.279194
• Zeng, Zheng and Goodman, Rodney M., el al. (1993) Learning finite state machines with self-clustering recurrent networks; Neural Computation; Vol. 5; No. 6; 976-990; 10.1162/neco.1993.5.6.976
• Miller, John W. and Goodman, Rod, el al. (1993) On loss functions which minimize to conditional expected values and posterior probabilities; IEEE Transactions on Information Theory; Vol. 39; No. 4; 1404-1408; 10.1109/18.243457
• Zeng, Zheng and Goodman, Rodney M., el al. (1993) Self-clustering recurrent networks; ISBN 0780309995; IEEE International Conference on Neural Networks, 1993; 33-38; 10.1109/icnn.1993.298535
• Goodman, Rodney M. and Higgins, Charles M., el al. (1992) Rule-based neural networks for classification and probability estimation; Neural Computation; Vol. 4; No. 6; 781-804; 10.1162/neco.1992.4.6.781
• Smyth, Padhraic and Goodman, Rodney M. (1992) An information theoretic approach to rule induction from databases; IEEE Transactions on Knowledge and Data Engineering; Vol. 4; No. 4; 301-316; 10.1109/69.149926
• Miller, John W. and Goodman, Rod, el al. (1991) Objective functions for probability estimation; ISBN 0780301641; IJCNN-91-Seattle International Joint Conference on Neural Networks; 881-886; 10.1109/ijcnn.1991.155295
• Goodman, Rod and Miller, John W., el al. (1991) Objective Functions For Neural Network Classifier Design; ISBN 0-7803-0056-4; Proceedings. 1991 IEEE International Symposium on Information Theory; 87; 10.1109/ISIT.1991.695143
• Goodman, Rodney M. and Miller, John W., el al. (1989) An Information Theoretic Approach to Modeling Neural Network Expert Systems; 10.1109/ITW.1989.761436
• Goodman, Rodney M. and Miller, John W., el al. (1989) An Information Theoretic Approach to Rule-Based Connectionist Expert Systems; ISBN 1-558-60015-9; Advances in Neural Information Processing Systems 1 (NIPS 1988); 256-263
• Goodman, Rodney M. and Smyth, Padhraic (1988) Decision tree design from a communication theory standpoint; IEEE Transactions on Information Theory; Vol. 34; No. 5; 979-994; 10.1109/18.21221