MLJ 1995

41 papers

A Branch and Bound Incremental Conceptual Clusterer Arthur J. Nevins

A Comparison of ID3 and Backpropagation for English Text-to-Speech Mapping Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri

Alecsys and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems Marco Dorigo

An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms Dietrich Wettschereck, Thomas G. Dietterich

An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Jianping Zhang, Ryszard S. Michalski

Automated Refinement of First-Order Horn-Clause Domain Theories Bradley L. Richards, Raymond J. Mooney

Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers Paul W. Goldberg, Mark Jerrum

Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition Arie Ben-David, Janice Mandel

Comprehension Grammars Generated from Machine Learning of Natural Languages Patrick Suppes, Michael Böttner, Lin Liang

Declarative Bias for Specific-to-General ILP Systems Hilde Adé, Luc De Raedt, Maurice Bruynooghe

Dexter: A System That Experiments with Choices of Training Data Using Expert Knowledge in the Domain of DNA Hydration Dawn M. Cohen, Casimir A. Kulikowski, Helen Berman

Discovering Dependencies via Algorithmic Mutual Information: A Case Study in DNA Sequence Comparisons Aleksandar Milosavljevic

Encouraging Experimental Results on Learning CNF Raymond J. Mooney

Evaluation and Selection of Biases in Machine Learning Diana F. Gordon, Marie desJardins

Genetic Algorithms, Operators, and DNA Fragment Assembly Rebecca J. Parsons, Stephanie Forrest, Christian Burks

Inductive Policy: The Pragmatics of Bias Selection Foster J. Provost, Bruce G. Buchanan

Inferring Finite Automata with Stochastic Output Functions and an Application to mAP Learning Thomas L. Dean, Dana Angluin, Kenneth Basye, Sean P. Engelson, Leslie Pack Kaelbling, Evangelos Kokkevis, Oded Maron

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data David Heckerman, Dan Geiger, David Maxwell Chickering

Learning Binary Relations Using Weighted Majority Voting Sally A. Goldman, Manfred K. Warmuth

Learning Fallible Deterministic Finite Automata Dana Ron, Ronitt Rubinfeld

Learning from a Population of Hypotheses Michael J. Kearns, H. Sebastian Seung

Machine Discovery of Protein Motifs Darrell Conklin

Monotonicity Maintenance in Information-Theoretic Machine Learning Algorithms Arie Ben-David

Multivariate Decision Trees Carla E. Brodley, Paul E. Utgoff

Neural Networks for Full-Scale Protein Sequence Classification: Sequence Encoding with Singular Value Decomposition Cathy H. Wu, Michael W. Berry, Sailaja Shivakumar

On Polynomial-Time Learnability in the Limit of Strictly Deterministic Automata Takashi Yokomori

On the Complexity of Function Learning Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger

On the Learnability of Disjunctive Normal Form Formulas Howard Aizenstein, Leonard Pitt

On the Stochastic Complexity of Learning Realizable and Unrealizable Rules Ronny Meir, Neri Merhav

Piecemeal Learning of an Unknown Environment Margrit Betke, Ronald L. Rivest, Mona Singh

Probably Almost Discriminative Learning Kenji Yamanishi

Recursive Automatic Bias Selection for Classifier Construction Carla E. Brodley

Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension Sally Floyd, Manfred K. Warmuth

Searching for Representations to Improve Protein Sequence Fold-Class Prediction Thomas R. Ioerger, Larry A. Rendell, Shankar Subramaniam

Shifting Vocabulary Bias in Speedup Learning Devika Subramanian

Support-Vector Networks Corinna Cortes, Vladimir Vapnik

Technical Note: Bias and the Quantification of Stability Peter D. Turney

The Appropriateness of Predicate Invention as Bias Shift Operation in ILP Irene Stahl

The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces Andrew W. Moore, Christopher G. Atkeson

Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization Timothy L. Bailey, Charles Elkan

Use of Adaptive Networks to Define Highly Predictable Protein Secondary-Structure Classes Alan S. Lapedes, Evan W. Steeg, Robert M. Farber