 | Editing by example - R. Nix - 1984 |
| Editing By Example (Ph.D Thesis) - R. Nix - 1983 |
| Editorial - Arun Sharma - 2000 |
| Editorial - Doug Fisher - 2002 |
| Editorial: Human and Machine Learning - Pat Langley - 1986 |
| Editorial: Inductive Logic Programming is Coming of Age - Peter Flach and Saso Dzeroski - 2001 |
| Editorial: Kernel Methods: Current Research and Future Directions - Nello Cristianini, Colin Campbell and Chris Burges - 2002 |
| Editorial: The Terminology of Machine Learning - Pat Langley - 1986 |
| Editors' foreword - William Gasarch and Ming Li - 1996 |
| Editors' Introduction - Klaus P. Jantke, Takeshi Shinohara and T. Zeugmann - 1995 |
| Editors' Introduction - Nicolo Cesa-Bianchi, Masayuki Numao and Rüdiger Reischuk - 2002 |
| Editors' Introduction - Michael M. Richter, Carl H. Smith, Rolf Wiehagen and Thomas Zeugmann - 1998 |
| Editors' Introduction - Naoki Abe, Roni Khardon and Thomas Zeugmann - 2001 |
| Effective and Efficient Knowledge Base Refinement - Leonardo Carbonara and Derek Sleeman - 1999 |
| Effective Learning in Dynamic Environments by Explicit Context Tracking - Gerhard Widmer and Miroslav Kubat - 1993 |
| An effective model for grammar inference - S. Crespi-Reghizzi - 1972 |
| The effective size of a neural network: A principal component approach - David W. Opitz - 1997 |
| The effect of adding relevance information in a relevance feedback environment - C. Buckley, G. Salton and J. Allan - 1994 |
| The Effect of Instance-Space Partition on Significance - Jeffrey P. Bradford and Carla E. Brodley - 2001 |
| The Effect of Multiple Knowledge Sources on Learning and Teaching - R. Hall, E. Wenger, D. Kibler and P. Langley - 1985 |
| The Effect of Noise on Concept Learning - J. R. Quinlan - 1986 |
| The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures - Marcel Turcotte, Stephen H. Muggleton and Michael J. E. Sternberg - 2001 |
| The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms - Sven Koenig and Reid G. Simmons - 1996 |
| The Effect of the Input Density Distribution on Kernel-based Classifiers - Christopher K. I. Williams and Matthias Seeger - 2000 |
| The Effects of Differnet Feature Sets on the Web Page Categorization Problem Using the Iterative Cross-Training Algorithm - Nuanwan Soonthornphisaj and Boonserm Kijsirikul - 2001 |
| Effects of feature selection with 'blurring' on neurofuzzy systems - Selwyn Piramuthu - 1996 |
| Effects of Kolmogorov complexity present in inductive inference as well - Andris Ambainis, Kalvis Aps\=ıtis, Cristian Calude, Rīsiņš Freivalds, Marek Karpinski, Tomas Larfeldt, Iveta Sala and Juris Smotrovs - 1997 |
| The effects of training set size on decision tree complexity - Tim Oates and David Jensen - 1997 |
| Efficient agnostic PAC-learning with simple hypotheses - W. Maass - 1994 |
| Efficient Algorithm for Learning Simple Regular Expressions from Noisy Examples - Alvis Brāzma - 1994 |
| An efficient algorithm for optimal pruning of decision trees - Hussein Almuallim - 1996 |
| Efficient algorithms for decision tree cross-validation - Hendrik Blockeel and Jan Struyf - 2001 |
| Efficient algorithms for finding multi-way splits for decision trees - Truxton Fulton, Simon Kasif and Steven Salzberg - 1995 |
| Efficient algorithms for learning to play repeated games against computationally bounded adversaries - Yoav Freund, Michael Kearns, Yishay Mansour, Dana Ron and Ronitt Rubinfeld - 1995 |
| Efficient algorithms for minimizing cross validation error - Andrew W. Moore and Mary S. Lee - 1994 |
| Efficient Algorithms for the Inference of Minimum Size DFAs - Arlindo L. Oliveira and João P. M. Silva - 2001 |
| Efficient algorithms with neural networks behavior - S. Omohundro - 1987 |
| Efficient Ambiguity Detection in C-NFA, a Step Towards the Inference on Non Deterministic Automata - François Coste and Daniel Fredouille - 2000 |
| An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoner - Barry Smyth and Elizabeth McKenna - 2000 |
| An Efficient Approach for Approximating Multi-Dimensional Range Queries and Nearest Neighbor Classification in Large Datasets - Carlotta Domeniconi and Dimitrios Gunopulos - 2001 |
| Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables - David Maxwell Chickering and David Heckerman - 1997 |
| An efficient boosting algorithm for combining preferences - Yoav Freund, Raj Iyer, Robert E. Schapire and Yoram Singer - 1998 |
| Efficient Construction of Regression Trees with Range and Region Splitting - Yasuhiko Morimoto, Hiromu Ishii and Shinichi Morishita - 2001 |
| Efficient Data Mining by Active Learning - Hiroshi Mamitsuka and Naoki Abe - 2001 |
| Efficient Data Mining from Large Text Databases - Hiroki Arimura, Hiroshi Sakamoto and Setsuo Arikawa - 2001 |
| Efficient distribution-free learning of probabilistic concepts - Michael J. Kearns and Robert E. Schapire - 1994 |
| Efficient distribution-free learning of probabilistic concepts - Michael J. Kearns and Robert E. Schapire - 1994 |
| Efficient distribution-free population learning of simple concepts - Atsuyoshi Nakamura, Jun-ichi Takeuchi and Naoki Abe - 1998 |
| An efficient exact learning algorithm for ordered binary decision diagrams - Atsuyoshi Nakamura - 1997 |
| An Efficient Extension to Mixture Techniques for Prediction and Decision Trees - Fernando C. N. Pereira and Yoram Singer - 1999 |
| Efficient feature selection in conceptual clustering - Mark Devaney and Ashwin Ram - 1997 |
| Efficient identification of regular expressions from representative examples - A. Brāzma - 1993 |
| Efficient Incremental Induction of Decision Trees - Dimitrios Kalles and Tim Morris - 1996 |
| Efficient inductive inference of primitive prologs from positive data - Hiroki Ishizaka, Hiroki Arimura and Takeshi Shinohara - 1993 |
| Efficient inference of partial types - Dexter Kozen, Jens Palsberg and Michael I. Schwartzbach - 1994 |
| Efficient learning from delayed rewards through symbolic evolution - David E. Moriarty and Risto Miikkulainen - 1995 |
| Efficient learning of context-free grammars from positive structural examples - Yasubumi Sakakibara - 1992 |
| Efficient learning of continuous neural networks - P. Koiran - 1994 |
| Efficient learning of monotone concepts via quadratic optimization - David Gamarnik - 1998 |
| Efficient Learning of One-Variable Pattern Languages from Positive Examples - T. Erlebach, P. Rossmanith, H. Stadtherr, A. Steger and T. Zeugmann - 1996 |
| Efficient Learning of Real Time One-Counter Automata - Amr F. Fahmy and Robert S. Roos - 1995 |
| Efficient learning of real time two-counter automata - Amr F. Fahmy and Robert S. Roos - 1996 |
| Efficient learning of regular expressions from approximate examples - Alvis Brāzma - 1997 |
| Efficient Learning of Regular Expressions from Good Examples - Alvis Brāzma and Kārlis Čerāns - 1994 |
| Efficient learning of selective Bayesian network classifiers - Moninder Singh and Gregory M. Provan - 1996 |
| Efficient Learning of Semi-structured Data from Queries - Hiroki Arimura, Hiroshi Sakamoto and Setsuo Arikawa - 2001 |
| Efficient learning of typical finite automata from random walks - Y. Freund, M. Kearns, D. Ron, R. Rubinfeld, R. Schapire and L. Sellie - 1993 |
| Efficient Learning Trough Evolution: Neural Programming and Internal Reinforcement - Astro Teller and Manuela Veloso - 2000 |
| Efficient learning with virtual threshold gates - Wolfgang Maass and Manfred K. Warmuth - 1995 |
| Efficient locally weighted polynomial regression predictions - Andrew W. Moore, Jeff Schneider and Kan Deng - 1997 |
| Efficiently Approximating Weighted Sums with Exponentially Many Terms - Deepak Chawla, Lin Li and Stephen Scott - 2001 |
| Efficiently Determining the Starting Sample Size for Progressive Sampling - Baohua Gu, Bing Liu, Feifang Hu and Huan Liu - 2001 |
| An efficient membership-query algorithm for learning DNF with respect to the uniform distribution - Jeffrey C. Jackson - 1997 |
| Efficient memory-based dynamic programming - Jing Peng - 1995 |
| Efficient Methods for Massively Parallel Symbolic Induction: Algorithms and Implementation - R. H. Lathrop - June 1990 |
| An Efficient Method To Estimate Bagging's Generalization Error - David Wolpert and William G. Macready - 1999 |
| Efficient Mining from Large Database by Query Learning - Hiroshi Mamitsuka and Naoki Abe - 2000 |
| Efficient NC algorithms for set cover with applications to learning and geometry - Bonnie Berger, John Rompel and Peter W. Shor - 1994 |
| Efficient NC algorithms for set cover with applications to learning and geometry - B. Berger, J. Rompel and P. W. Shor - 1989 |
| Efficient noise-tolerant learning from statistical queries - M. Kearns - 1993 |
| Efficient non-linear control by combining Q-learning with local linear controllers - Hajime Kimura and Shigenobu Kobayashi - 1999 |
| An Efficient PAC Algorithm for Reconstructing a Mixture of Lines - Sanjoy Dasgupta, Elan Pavlov and Yoram Singer - 2002 |
| An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery - Michael R. Brent - 1999 |
| Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries - Carlos Domingo, Nina Mishra and Leonard Pitt - 1999 |
| Efficient reinforcement learning - C. N. Fiechter - 1994 |
| Efficient Reinforcement Learning through Symbiotic Evolution - David E. Moriarty and Risto Miikkulainen - 1996 |
| An Efficient Robust Algorithm for Learning Decision Lists - Y. Sakakibara - August 1990 |
| Efficient Specialization of Relational Concepts - Kurt Vanlehn - 1989 |
| An efficient subsumption algorithm for inductive logic programming - Jörg-Uwe Kietz and Marcus Lübbe - 1994 |
| Efficient SVM Regression Training with SMO - Gary William Flake and Steve Lawrence - 2002 |
| Efficient unsupervised learning - P. D. Laird - 1988 |
| Elementary formal system as a unifying framework for language learning - S. Arikawa, T. Shinohara and A. Yamamoto - 1989 |
| Elementary formal systems, intrinsic complexity, and procrastination - Sanjay Jain and Arun Sharma - 1997 |
| Elements of Information Theory - T. Cover and J. Thomas - 1991 |
| Elements of Scientific Inquiry - Eric Martin and Daniel N. Osherson - 1998 |
| Elevator Group Control Using Multiple Reinforcement Learning Agents - Robert H. Crites and Andrew G. Barto - 1998 |
| Eligibility Traces for Off-Policy Policy Evaluation - Doina Precup, Richard S. Sutton and Satinder Singh - 2000 |
| The EM algorithm and Information geometry in neural network learning - S. Amari - January 1995 |
| EM Algorithm with Split and Merge Operations for Mixture Models - Naonori Ueda and Ryohei Nakano - 2000 |
| EM Learning for Symbolic-Statistical Models in Statistical Abduction - Taisuke Sato - 2001 |
| Empirical Bayes for Learning to Learn - Tom Heskes - 2000 |
| An Empirical Comparison of Pruning Methods for Decision Tree Induction - John Mingers - 1989 |
| An Empirical Comparison of Selection Measures for Decision-Tree Induction - John Mingers - 1989 |
| An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants - Eric Bauer and Ron Kohavi - 1999 |
| An empirical investigation of brute force to choose features, smoothers and function approximators - Andrew W. Moore, Daniel J. Hill and Michael P. Johnson - 1995 |
| Empirical Learning as a Function of Concept Character - Larry Rendell and Howard Cho - 1990 |
| Empirical Learning Using Rule Threshold Optimization for Detection of Events in Synthetic Images - David J. Montana - 1990 |
| An Empirical Study of MetaCost Using Boosting Algorithms - Kai Ming Ting - 2000 |
| Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain - Avrim Blum - 1997 |
| Empirical support for Winnow and weighted-majority based algorithms: results on a calendar scheduling domain - Avrim Blum - 1995 |
| Employing EM and pool-based active learning for text classification - Andrew Kachites McCallum and Kamal Nigam - 1998 |
| Encouraging Experimental Results on Learning CNF - Raymond J. Mooney - 1995 |
| Enhancing Supervised Learning with Unlabeled Data - Sally Goldman and Yan Zhou - 2000 |
| Enhancing the Plausibility of Law Equation Discovery - Takashi Washio, Hiroshi Motoda and Yuji Niwa - 2000 |
| Enlarging the Margins in Perceptron Decision Trees - Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor and Donghui Wu - 2000 |
| Entropy, Combinatorial Dimensions and Random Averages - Shahar Mendelson and Roman Vershynin - 2002 |
| Entropy Numbers of Linear Function Classes - Robert C. Williamson, Alex J. Smola and Bernhard Schölkopf - 2000 |
| Entropy Numbers, Operators and Support Vector Kernels - Robert C. Williamson, Alex J. Smola and Bernhard Schölkopf - 1999 |
| Entropy Optimization Principles with Applications - J. N. Kapur and H. K Kesavan - 1992 |
| Enumerable Classes of Total Recursive Functions: Complexity of Inductive Inference - Andris Ambainis and Juris Smotrovs - 1994 |
| Epsilon-nets and Simplex Range Queries - D. Haussler and E. Welzl - 1987 |
| The equivalence and learning of probabilistic automata - W. Tzeng - 1989 |
| Equivalence of Models for Polynomial Learnability - D. Haussler, M. Kearns, N. Littlestone and M. K. Warmuth - December 1991 |
| Equivalence queries and approximate fingerprints - D. Angluin - 1989 |
| Equivalence Queries and DNF formulas - D. Angluin - November 1988 |
| Errata - No Author - 1990 |
| Errata to Extending - Authorless - 1990 |
| Erratum - No Author - 1993 |
| Erratum one - Authorless - 1989 |
| Erratum to Discovery - Authorless - 1993 |
| Error Analysis of Automatic Speech Recognition Using Principal Direction Divisive Partitioning - David McKoskey and Daniel Boley - 2000 |
| Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs - Thomas G. Dietterich and Ghulum Bakiri - 1991 |
| Error-correcting output coding corrects bias and variance - Eun Bae Kong and Thomas G. Dietterich - 1995 |
| Error detecting in inductive inference - R. Freivalds, E. B. Kinber and R. Wiehagen - 1995 |
| Error Reduction through Learning Multiple Descriptions - Kamal M. Ali and Michael J. Pazzani - 1996 |
| Estimating a Boolean Perceptron from Its Average Satisfying Assignment: A Bound on the Precision Required - Paul W. Goldberg - 2001 |
| Estimating a Kernel Fisher Discriminant in the Presence of Label Noise - Neil D. Lawrence and Bernhard Schölkopf - 2001 |
| Estimating a mixture of two product distributions - Yoav Freund and Yishay Mansour - 1999 |
| Estimating continuous distributions in Bayesian classifiers - George H. John and Pat Langley - 1995 |
| Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates - Tom Bylander - 2002 |
| Estimating the Generalization Performance of an SVM Efficiently - Thorsten Joachims - 2000 |
| Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces - Jürgen Forster, Niels Schmitt and Hans Ulrich Simon - 2001 |
| Estimating the Predictive Accuracy of a Classifier - Hilan Bensusan and Alexandros Kalousis - 2001 |
| Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning - Peter L. Bartlett and Jonathan Baxter - 2000 |
| Estimation of Dependences Based on Empirical Data - V. N. Vapnik - 1982 |
| Estimation of Time-Varying Parameters in Statistical Models: An Optimization Approach - Dimitris Bertsimas, David Gamarnik and John N. Tsitsiklis - 1999 |
| Evaluating the performance of a simple inductive procedure in the presence of overfitting error - A. Nobel - 1991 |
| Evaluation and selection of biases in machine learning - Diana F. Gordon and Marie desJardins - 1995 |
| Evaluation of Clinical Relevance of Clinical Laboratory Investigations by Data Mining - Ulrich Sack and Manja Kamprad - 2001 |
| Evaluation of learning biases using probabilistic domain knowledge - Marie desJardins - 1994 |
| The Evaluation of Predictive Learners: Some Theoretical and Empirical Results - Kevin B. Korb, Lucas R. Hope and Michelle J. Hughes - 2001 |
| An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning - Wojciech Kwedlo and Marek Kretowski - 2001 |
| An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata - Stefan Veeser - 2000 |
| Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State - Matthew R. Glickman and Katia Sycara - 2001 |
| Evolutionary Trees Can be Learned in Polynomial Time in the Two-State General Markov Model - Mary Cryan, Leslie Ann Goldberg and Paul W. Goldberg - 2001 |
| Evolution of a subsumption architecture that performs a wall following task for an autonomous mobile robot - John R. Koza - 1994 |
| Evolving structured programs with hierarchical instructions and skip nodes - Rafał Sałustowicz and Jürgen Schmidhuber - 1998 |
| Exact classification with two-layer neural nets - Gavin J. Gibson - 1996 |
| Exact identification of circuits using fixed points of amplification functions - S. A. Goldman, M. J. Kearns and R. E. Schapire - August 1993 |
| Exact Learning of Discretized Geometric Concepts - Nader H. Bshouty, Paul W. Goldberg, Sally A. Goldman and H. David Mathias - 1998 |
| Exact Learning of Formulas in Parallel - Nader H. Bshouty - 1997 |
| Exact learning of mu-DNF formulas with malicious membership queries - Dana Angluin - March 1994 |
| Exact learning of linear combinations of monotone terms from function value queries - Atsuyoshi Nakamura and Naoki Abe - 1995 |
| Exact learning of read-k disjoint DNF and not-so-disjoint DNF - H. Aizenstein and L. Pitt - 1992 |
| Exact learning of read-twice DNF formulas - H. Aizenstein and L. Pitt - 1991 |
| Exact learning of tree patterns from queries and counterexamples - Thomas R. Amoth, Paul Cull and Prasad Tadepalli - 1998 |
| Exact learning of unordered tree patterns from queries - Thomas R. Amoth, Paul Cull and Prasad Tadepalli - 1999 |
| Exact learning via teaching assistants - V. Arvind and N. V. Vinodchandran - 2000 |
| Exact learning via teaching assistants - V. Arvind and N. V. Vinodchandran - 1997 |
| Exact learning via the monotone theory - Nader H. Bshouty - 1993 |
| Exact learning when irrelevant variables abound - D. Guijarro, V. Lavin and V. Raghavan - 1999 |
| Exactly Learning Automata of Small Cover Time - Dana Ron and Ronitt Rubinfeld - 1997 |
| An exact probability metric for decision tree splitting and stopping - J. Kent Martin - 1997 |
| Exemplar-Based Knowledge Acquisition - Yoram Reich - 1991 |
| Exemplar-based learning: theory and implementation - S. Salzberg - October 1988 |
| Expectation Maximization for Weakly Labeled Data - Yuri Ivanov, Bruce Blumberg and Alex Pentland - 2001 |
| Expected error analysis for model selection - Tobias Scheffer and Thorsten Joachims - 1999 |
| Experience Selection and Problem Choice in an Exploratory Learning System - Paul D. Scott and Shaul Markovitch - 1993 |
| Experience with a Learning Personal Assistant - Tom M. Mitchell, Rich Caruana, Dayne Freitag, John P. McDermott and David Zabowski - 1994 |
| An Experimental and Theoretical Comparison of Model Selection Methods - Michael Kearns, Yishay Mansour, Andrew Y. Ng and Dana Ron - 1997 |
| An Experimental Comparison of Connectionist and Conventional Classification Systems on Natural Data - P. C. Woodland and S. G. Smyth - October 1989 |
| An Experimental Comparison of Model-Based Clustering Methods - Marina Meila and David Heckerman - 2001 |
| An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms - Dietrich Wettschereck and Thomas G. Dietterich - 1995 |
| An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization - Thomas G. Dietterich - 2000 |
| An experimental evaluation of coevolutive concept learning - Cosimo Anglano, Attilio Giordana, Giuseppe Lo Bello and Lorenza Saitta - 1998 |
| An Experimental Evaluation of Integrating Machine Learning with Knowledge - Geoffrey I. Webb, Jason Wells and Zijian Zheng - 1999 |
| Experimental Goal Regression: A Method for Learning Problem-Solving Heuristics - Bruce W. Porter and Dennis F. Kibler - 1986 |
| Experimental knowledge acquisition for planning - Kang Soo Tae and Diane J. Cook - 1996 |
| Experimental Results on Q-Learning for General-Sum Stochastic Games - Junling Hu and Michael P. Wellman - 2000 |
| Experiments on the transfer of knowledge between neural networks - Lorien Y. Pratt - 1994 |
| Experiments with a new Boosting algorithm - Yoav Freund and Robert E. Schapire - 1996 |
| Experiments with Incremental Concept Formation: UNIMEM - Michael Lebowitz - 1987 |
| Experiments with noise filtering in a medical domain - Dragan Gamberger, Nada Lavrač and Ciril Grošelj - 1999 |
| Explanation-Based Generalization: A Unifying View - Tom M. Mitchell, Richard M. Keller and Smadar T. Kedar-Cabelli - 1986 |
| Explanation-Based Learning: An Alternative View - Gerald Dejong and Raymond Mooney - 1986 |
| Explanation-based learning and reinforcement learning: a unified view - Thomas G. Dietterich and Nicholas S. Flann - 1997 |
| Explanation-Based Learning for Diagnosis - Yousri El Fattah and Paul O'Rorke - 1993 |
| Explanation-Based Reuse of Prolog Programs - Yasuyuki Koga, Eiju Hirowatari and Setsuo Arikawa - 1994 |
| Explicit Representation of Concept Negation - Jean-Francois Puget - 1994 |
| Exploiting Chaos to Predict the Future and Reduce Noise - J. D. Farmer and J. J. Sidorowich - March 1988 |
| Exploiting Classifier Combination for Early Melanoma Diagnosis Support - Enrico Blanzieri, C. Eccher, S. Forti and A. Sboner - 2000 |
| Exploiting Context When Learning to Classify - Peter D. Turney - 1993 |
| Exploiting random walks for learning - P. L. Bartlett, P. Fischer and K.-U. Höffgen - 1994 |
| Exploiting the Cost of (In)sensitivity of Decision Tree Splitting Criteria - Chris Drummond and Robert C. Holte - 2000 |
| Exploiting the omission of irrelevant data - Russell Greiner, Adam J. Grove and Alexander Kogan - 1996 |
| Exploration Bonuses and Dual Control - Peter Dayan and Terrence J. Sejnowski - 1996 |
| Exploration control in Reinforcement Learning Using Optimistic Model Selection - Jeremy L. Wyatt - 2001 |
| Exploration of Multi-State Environments: Local Measures and Back-Propagation of Uncertainty - Nicolas Meuleau and Paul Bourgine - 1999 |
| Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises - J. L. McClelland and D. E. Rumelhart - 1988 |
| Explorations of an Incremental, Bayesian Algorithm for Categorization - John R. Anderson and Michael Matessa - 1992 |
| Exploratory Research in Machine Learning - Thomas G. Dietterich - 1990 |
| Exploring an Unknown Graph - X. Deng and C. H. Papadimitriou - 1990 |
| Exploring Learnability between Exact and PAC - Nader H. Bshouty, Jeffrey C. Jackson and Christino Tamon - 2002 |
| Exploring the decision forest: an empirical investigation of Occam's razor in decision tree induction - Patrick M. Murphy and Michael J. Pazzani - 1997 |
| Exploring Unknown Environments - Susanne Albers and Monika R. Henzinger - 1999 |
| Exponentially many local minima for single neurons - Peter Auer, Mark Herbster and Manfred K. Warmuth - 1996 |
| Exponentiated gradient methods for reinforcement learning - Doina Precup and Richard S. Sutton - 1997 |
| Exponentiated Gradient Versus Gradient Descent for Linear Predictors - J. Kivinen and M. K. Warmuth - June 1994 |
| Extended Association Algorithm Based on ROC Analysis for Visual Information Navigator - Hiroyuki Kawano and Minoru Kawahara - 2001 |
| Extended Stochastic Complexity and Minimax Relative Loss Analysis - Kenji Yamanishi - 1999 |
| Extending Domain Theories: Two Case Studies in Student Modeling - D. Sleeman, H. Hirsh, I. Ellery and In-Yung Kim - 1990 |
| Extending Elementary Formal Systems - Steffen Lange, Gunter Grieser and Klaus P. Jantke - 2001 |
| Extending the Valiant learning model - J. Amsterdam - 1988 |
| Extensional set learning - Sebastiaan A. Terwijn - 1999 |
| Extension of the PAC framework to finite and countable Markov chains - David Gamarnik - 1999 |
| Extensions of a Theory of Networks for Approximation and Learning: dimensionality and reduction and clustering - T. Poggio and F. Girosi - April 1990 |
| Extracting Best Consensus Motifs from Positive and Negative Examples - Erika Tateishi, Osamu Maruyama and Satoru Miyano - 1996 |
| Extracting Context-Sensitive Models in Inductive Logic Programming - Ashwin Srinivasan - 2001 |
| Extracting Hidden Context - Michael Bonnell Harries, Claude Sammut and Kim Horn - 1998 |
| Extracting Information from the Web for Concept Learning and Collaborative Filtering - William W. Cohen - 2000 |
| Extracting Refined Rules from Knowledge-Based Neural Networks - Geoffrey G. Towell and Jude W. Shavlik - 1993 |
| Extraction of Knowledge from Data Using Constrained Neural Networks - Raqui Kane, Irina Tchoumatchenko and Maurice Milgram - 1993 |
| Extraction of Primitive Motion and Discovery of Association Rules from Human Motion Data - Kuniaki Uehara and Mitsuomi Shimada - 2001 |
| Extraction of Recurrent Patterns from Stratified Ordered Trees - Jean-Gabriel Ganascia - 2001 |
| Extraction of Signal from High Dimensional Time Series: Analysis of Ocean Bottom Seismograph Data - Genshiro Kitagawa, Tetsuo Takanami, Asako Kuwano, Yoshio Murai and Hideki Shimamura - 2001 |
| Extremes in the Degrees of Inferability - L. Fortnow, W. Gasarch, S. Jain, E. Kinber, M. Kummer, S. Kurtz, M. Pleszkoch, T. Slaman, R. Solovay and F. Stephan - 1994 |
