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| Understanding Probabilistic Classifiers - Ashutosh Garg and Dan Roth - 2001 |
| Understanding the Nature of Learning: Issues and Research Directions - R. M. Michalski - 1986 |
| A Unified Approach to Inductive Logic and Case-Based Reasoning - Michael M. Richter - 1994 |
| A Unified Bias-Variance Decomposition and its Applications - Pedro Domingos - 2000 |
| A Unified Framework for Evaluation Metrics in Classification Using Decision Trees - Ricardo Vilalta, Mark Brodie, Daniel Oblinger and Irina Rish - 2001 |
| A Unified Loss Function in Bayesian Framework for Support Vector Regression - Wei Chu, S. Sathiya Keerthi and Chong Jin Ong - 2001 |
| Uniform and Non Uniform Predictability - R. Freivalds - 1974 |
| Uniform Characterizations of polynomial-query learnabilities - Yosuke Hayashi, Satoshi Matsumoto, Ayumi Shinohara and Masayuki Takeda - 1998 |
| Uniform Characterizations of Various Kinds of Language Learning - Shyam Kapur - 1993 |
| Uniform-distribution attribute noise learnability - Nader H. Bshouty, Jeffrey C. Jackson and Christino Tamon - 1999 |
| A Unifying Approach to HTML Wrapper Representation and Learning - Gunter Grieser, Klaus P. Jantke, Steffen Lange and Bernd Thomas - 2000 |
| A Unifying Approach to Monotonic Language Learning on Informant - S. Lange and T. Zeugmann - 1992 |
| Unifying Learning Methods by Colored Digraphs - Kenichi Yoshida, Hiroshi Motoda and Nitin Indurkhya - 1993 |
| Unions of identifiable families of languages - Kalvis Apsitis, Rusins Freivalds, Raimonds Simanovskis and Juris Smotrovs - 1996 |
| Universal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks - K. Hornik, M. Stinchcombe and H. White - 1990 |
| Universal Distributions and Time-Bounded Kolmogorov Complexity - Rainer Schuler - 1999 |
| Universal forecasting algorithms - V. Vovk - 1992 |
| A Universal Generalization for Temporal-Difference Learning Using Haar Basis Functions - Susumu Katayama, Hajime Kimura and Shigenobu Kobayashi - 2000 |
| Universal Goal Seekers - E. M. Gold - 1971 |
| Universal Grammar and Learnability Theory: The Case of Binding Domains and the `Subset Principle' - S. Kapur, B. Lust, W. Harbert and G. Motohardjono - 1993 |
| A Universal Inductive Inference Machine - Daniel N. Osherson, Michael Stob and Scott Weinstein - June 1991 |
| A Universal Method of Scientific Inquiry - Daniel N. Osherson, Michael Stob and Scott Weinstein - 1992 |
| Universal Portfolios - T. M. Cover - 1991 |
| Universal portfolio selection - V. Vovk and C. Watkins - 1998 |
| Universal Portfolios With and Without Transaction Costs - Avrim Blum and Adam Kalai - 1999 |
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| Unpacking Multi-valued Symbolic Features and Classes in Memory-based Language Learning - Antal van den Bosch and Jakub Zavrel - 2000 |
| Unsupervised Learning by Probabilistic Latent Semantic Analysis - Thomas Hofmann - 2001 |
| Unsupervised learning for mobile robot navigation using probabilistic data association - Ingemar J. Cox and John J. Leonard - 1994 |
| Unsupervised learning in neural computation - Erkki Oja - 2002 |
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| Unsupervised learning of multiple motifs in biopolymers using expectation maximization - Timothy L. Bailey and Charles Elkan - 1995 |
| Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming - Dimitar Kazakov and Suresh Manandhar - 2001 |
| Unsupervised learning using MML - Jonathan J. Oliver, Rohan A. Baxter and Chris S. Wallace - 1996 |
| Unsupervised Sequence Segentation by a Mixture of Switching Variable Memory Markov Sources - Yevgeni Seldin, Gill Bejerano and Naftali Tishby - 2001 |
| Unsupervised visual learning of three-dimensional objects using a modular network architecture - S. Suzuki H. Ando and T. Fujita - 1999 |
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| Upper and Lower Bounds on the Learning Curve for Gaussian Processes - Christopher K. I. Williams and Francesco Vivarelli - 2000 |
| An upper bound on the loss from approximate optimal-value functions - Satinder P. Singh and Richard C. Yee - 1994 |
| The use of abstract primitives in representing the meaning of Verbs for understanding metaphors - M. Suwa and H. Motoda - 1992 |
| Use of Adaptive Networks to Define Highly Predictable Protein Secondary-Structure Classes - Alan S. Lapedes, Evan W. Steeg and Robert M. Farber - 1995 |
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| Use of Reduction Arguments in Determining Popperian FIN-Type Learning Capabilities - Robert Daley and Bala Kalyanasundaram - 1993 |
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| Using a permutation test for attribute selection in decision trees - Eibe Frank and Ian H. Witten - 1998 |
| Using a Symbolic Machine Learning Tool to Refine Lexico-syntactic Patterns - Emmanuel Morin and Emmanuelle Martienne - 2000 |
| Using Attribute Grammars for Description of Inductive Inference Search Space - Uğis Sarkans and J. Bārzdiņs - 1998 |
| Using communication to reduce locality in distributed multiagent learning - Maja J. Mataric - 1998 |
| Using Computational Learning Strategies as a Tool for Combinatorial Optimization - Andreas Birkendorf and Hans-Ulrich Simon - 1998 |
| Using Correspondence Analysis to Combine Classifiers - Christopher J. Merz - 1999 |
| Using Decision Trees to Construct a Practical Parser - Masahiko Haruno, Satoshi Shirai and Yoshifumi Ooyama - 1999 |
| Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families - M. P. Brown, R. Hughey, A. Krogh, I. S. Mian, K. Sjölander and D. Haussler - July 1993 |
| Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error - Gabriele Zenobi and Pádraig Cunningham - 2001 |
| Using domain information during the learning of a subsequential transducer - José Oncina and Miguel Angel Varó - 1996 |
| Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery - Ljupco Todorovski and Saso Dzeroski - 2001 |
| Using eligibility traces to find the best memoryless policy in partially observable Markov decision processes - John Loch and Satinder Singh - 1998 |
| Using EM to Learn 3D Models of Indoor Environments with Mobile Robots - Yufeng Lui, Rosemary Emery, Deepayan Charabarti, Wolfram Burgard and Sebastian Thrun - 2001 |
| Using Error-Correcting Codes for Text Classification - Rayid Ghani - 2000 |
| Using Experts for Predicting Continuous Outcomes - J. Kivinen and M. Warmuth - 1994 |
| Using Genetic Algorithms for Concept Learning - Kenneth A. De Jong, William M. Spears and Diana F. Gordon - 1993 |
| Using genetic search to refine knowledge-based neural networks - David W. Opitz and Jude W. Shavlik - 1994 |
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| Using Heuristics to Speed up Induction on Continuous-Valued Attributes - G. Seidelmann - 1993 |
| Using Iterated Bagging to Debias Regressions - Leo Breiman - 2001 |
| Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding - Richard Maclin and Jude W. Shavlik - 1993 |
| Using knowledge-based neural networks to refine roughly-correct information - Geoffrey G. Towell and Jude W. Shavlik - 1994 |
| Using knowledge to improve N-gram language modelling through the MGGI methodology. - Enrique Vidal and David Llorens - 1996 |
| Using Knowledge to Speed Learning: A Comparison Knowledge-based Cascade-correlation and Multi-task Learning - Thomas R. Shultz and Francois Rivest - 2000 |
| Using Kullback-Leibler Divergence in Learning Theory - S. Anoulova and S. Pölt - 1994 |
| Using Learning by Discovery to Segment Remotely Sensed Images - Leen-Kiat Soh and Costas Tsatsoulis - 2000 |
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| Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing - Lappoon R. Tang and Raymond J. Mooney - 2001 |
| Using Multiple Levels of Learning and Diverse Evidence Sources to Uncover Coordinately Controlled Genes - Mark Craven, David Page, Jude Shavlik, Joseph Bockhorst and Jeremy Glasner - 2000 |
| Using Natural Language Processing and Discourse Features to Identify Understanding Errors in a Spoken Dialogue System - Marilyn Walker, Jerry Wright and Irene Langkilde - 2000 |
| Using neural networks to modularize software - Robert W. Schwanke and Joseé Stephen Hanson - 1994 |
| Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space - Shumeet Baluja and Scott Davies - 1997 |
| Using output codes to boost multiclass learning problems - Robert E. Schapire - 1997 |
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| Using reinforcement learning to spider the web efficiently - Jason Rennie and Andrew Kachites McCallum - 1999 |
| Using sampling and queries to extract rules from trained neural networks - Mark W. Craven and Jude W. Shavlik - 1994 |
| Using Subclasses to Improve Classification Learning - Achim Hoffmann, Rex Kwok and Paul Compton - 2001 |
| Using symbol clustering to improve probabilistic automaton inference - Pierre Dupont and Lin Chase - 1998 |
| Using telltales in developing program test sets - J. Cherniavsky and C. Smith - 1986 |
| Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor Classifier and to Select Relevant Attributes - Antonin Rozsypal and Miroslav Kubat - 2001 |
| Using the Minimum Description Length Principle to Infer Reduced Ordered Decision Graphs - Arlindo L. Oliveira and Alberto Sangiovanni-Vincentelli - 1996 |
| Using Upper Confidence Bounds for Online Learning - Peter Auer - 2000 |
| Using Vapnik-Chervonenkis Dimension to Analyze the Testing Complexity of Program Segments - Kathleen Romanik and Jeffrey Scott Vitter - August 1996 |
| The Utility of Knowledge in Inductive Learning - Michael Pazzani and Dennis Kibler - 1992 |
| The Utilization of Context Signals in the Analysis of ABR Potentials by Application of Neural Networks - Andrzej Izworski, Ryszard Tadeusiewicz and Andrzej Paslawski - 2000 |
