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Note: Links lead to the DBLP on the Web. Yoram Singer 52 Eleazar Eskin , William Stafford Noble , Yoram Singer: Protein Family Classification Using Sparse Markov Transducers. Journal of Computational Biology 10 (2): 187-214 (2003) 51 Sanjoy Dasgupta , Elan Pavlov , Yoram Singer: An Efficient PAC Algorithm for Reconstructing a Mixture of Lines. ALT 2002 : 351-364 50 Koby Crammer , Yoram Singer: A new family of online algorithms for category ranking. SIGIR 2002 : 151-158 49 Shai Shalev-Shwartz , Shlomo Dubnov , Nir Friedman , Yoram Singer: Robust temporal and spectral modeling for query By melody. SIGIR 2002 : 331-338 48 Eleazar Eskin , William Stafford Noble , Yoram Singer: Using Substitution Matrices to Estimate Probability Distributions for Biological Sequences. Journal of Computational Biology 9 (6): 775-792 (2002) 47 Koby Crammer , Yoram Singer: On the Learnability and Design of Output Codes for Multiclass Problems. Machine Learning 47 (2-3): 201-233 (2002) 46 Michael Collins , Robert E. Schapire , Yoram Singer: Logistic Regression, AdaBoost and Bregman Distances. Machine Learning 48 (1-3): 253-285 (2002) 45 Koby Crammer , Yoram Singer: Ultraconservative Online Algorithms for Multiclass Problems. COLT/EuroCOLT 2001 : 99-115 44 Eleazar Eskin , William Noble Grundy , Yoram Singer: Using mixtures of common ancestors for estimating the probabilities of discrete events in biological sequences. ISMB (Supplement of Bioinformatics) 2001 : 65-73 43 Koby Crammer , Yoram Singer: Pranking with Ranking. NIPS 2001 : 641-647 42 Yoram Singer: Guest Editor's Introduction. Machine Learning 43 (3): 71-172 (2001) 41 Raj D. Iyer , David D. Lewis , Robert E. Schapire , Yoram Singer, Amit Singhal : Boosting for Document Routing. CIKM 2000 : 70-77 40 Michael Collins , Robert E. Schapire , Yoram Singer: Logistic Regression, AdaBoost and Bregman Distances. COLT 2000 : 158-169 39 Koby Crammer , Yoram Singer: On the Learnability and Design of Output Codes for Multiclass Problems. COLT 2000 : 35-46 38 Peter Ju , Leslie Pack Kaelbling , Yoram Singer: State-based Classification of Finger Gestures from Electromyographic Signals. ICML 2000 : 439-446 37 Erin L. Allwein , Robert E. Schapire , Yoram Singer: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. ICML 2000 : 9-16 36 Eleazar Eskin , William Noble Grundy , Yoram Singer: Protein Family Classification Using Sparse Markov Transducers. ISMB 2000 : 134-145 35 Koby Crammer , Yoram Singer: Improved Output Coding for Classification Using Continuous Relaxation. NIPS 2000 : 437-443 34 Erin L. Allwein , Robert E. Schapire , Yoram Singer: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. Journal of Machine Learning Research 1 : 113-141 (2000) 33 Robert E. Schapire , Yoram Singer: BoosTexter: A Boosting-based System for Text Categorization. Machine Learning 39 (2/3): 135-168 (2000) 32 William W. Cohen , Yoram Singer: A Simple, Fast, and Effictive Rule Learner. AAAI/IAAI 1999 : 335-342 31 Yoram Singer: Leveraged Vector Machines. NIPS 1999 : 610-616 30 William W. Cohen , Robert E. Schapire , Yoram Singer: Learning to Order Things. JAIR 10 : 243-270 (1999) 29 Fernando C. N. Pereira , Yoram Singer: An Efficient Extension to Mixture Techniques for Prediction and Decision Trees. Machine Learning 36 (3): 183-199 (1999) 28 Robert E. Schapire , Yoram Singer: Improved Boosting Algorithms Using Confidence-rated Predictions. Machine Learning 37 (3): 297-336 (1999) 27 William W. Cohen , Yoram Singer: Context-Sensitive Learning Methods for Text Categorization. TOIS 17 (2): 141-173 (1999) 26 Robert E. Schapire , Yoram Singer: Improved Boosting Algorithms using Confidence-Rated Predictions. COLT 1998 : 80-91 25 Yoav Freund , Raj D. Iyer , Robert E. Schapire , Yoram Singer: An Efficient Boosting Algorithm for Combining Preferences. ICML 1998 : 170-178 24 Nir Friedman , Yoram Singer: Efficient Bayesian Parameter Estimation in Large Discrete Domains. NIPS 1998 : 417-423 23 Yoram Singer, Manfred K. Warmuth : Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy. NIPS 1998 : 578-584 22 Robert E. Schapire , Yoram Singer, Amit Singhal : Boosting and Rocchio Applied to Text Filtering. SIGIR 1998 : 215-223 21 Yoram Singer: Switching Portfolios. UAI 1998 : 488-495 20 Dana Ron , Yoram Singer, Naftali Tishby : On the Learnability and Usage of Acyclic Probabilistic Finite Automata. JCSS 56 (2): 133-152 (1998) 19 Shai Fine , Yoram Singer, Naftali Tishby : The Hierarchical Hidden Markov Model: Analysis and Applications. Machine Learning 32 (1): 41-62 (1998) 18 Fernando C. N. Pereira , Yoram Singer: An Efficient Extension to Mixture Techniques for Prediction and Decision Trees. COLT 1997 : 114-121 17 William W. Cohen , Robert E. Schapire , Yoram Singer: Learning to Order Things. NIPS 1997 16 Yoshua Bengio , Samy Bengio , Jean-Franc Isabelle , Yoram Singer: Shared Context Probabilistic Transducers. NIPS 1997 15 Yoav Freund , Robert E. Schapire , Yoram Singer, Manfred K. Warmuth : Using and Combining Predictors That Specialize. STOC 1997 : 334-343 14 Eric Bauer , Daphne Koller , Yoram Singer: Update Rules for Parameter Estimation in Bayesian Networks. UAI 1997 : 3-13 13 David P. Helmbold , Robert E. Schapire , Yoram Singer, Manfred K. Warmuth : A Comparison of New and Old Algorithms for a Mixture Estimation Problem. Machine Learning 27 (1): 97-119 (1997) 12 Yoram Singer: Adaptive Mixtures of Probabilistic Transducers. Neural Computation 9 (8): 1711-1733 (1997) 11 David P. Helmbold , Robert E. Schapire , Yoram Singer, Manfred K. Warmuth : On-Line Portfolio Selection Using Multiplicative Updates. ICML 1996 : 243-251 10 Yoram Singer, Manfred K. Warmuth : Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions. NIPS 1996 : 641-647 9 William W. Cohen , Yoram Singer: Context-sensitive Learning Methods for Text Categorization. SIGIR 1996 : 307-315 8 Dana Ron , Yoram Singer, Naftali Tishby : The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length. Machine Learning 25 (2-3): 117-149 (1996) 7 Dana Ron , Yoram Singer, Naftali Tishby : On the Learnability and Usage of Acyclic Probabilistic Finite Automata. COLT 1995 : 31-40 6 David P. Helmbold , Yoram Singer, Robert E. Schapire , Manfred K. Warmuth : A Comparison of New and Old Algorithms for a Mixture Estimation Problem. COLT 1995 : 69-78 5 Yoram Singer: Adaptive Mixture of Probabilistic Transducers. NIPS 1995 : 381-387 4 Hinrich Schütze , Yoram Singer: Part-of-Speech Tagging using a Variable Memory Markov Model. ACL 1994 : 181-187 3 Dana Ron , Yoram Singer, Naftali Tishby : Learning Probabilistic Automata with Variable Memory Length. COLT 1994 : 35-46 2 Dana Ron , Yoram Singer, Naftali Tishby : The Power of Amnesia. NIPS 1993 : 176-183 1 Yoram Singer, Naftali Tishby : Decoding Cursive Scripts. NIPS 1993 : 833-840  DiSC'03 © 2003 Association for Computing Machinery |
