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Association for the Advancement of Artificial Intelligence

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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 10 /

Learning

Learning: Constructive and Linguistic

  • A Connectionist Parser with Recursive Sentence Structure and Lexical Disambiguation

    George Berg

    32

    PDF
  • Learning to Disambiguate Relative Pronouns

    Claire Cardie

    38

    PDF
  • Discrimination-Based Constructive Induction of Logic Programs

    Boonserm Kijsirikul, Masayuki Numao, Masamichi Shimura

    44

    PDF
  • Learning Relations by Pathfinding

    Bradley L. Richards, Raymond J. Mooney

    50

    PDF

Learning: Discovery

  • Symmetry as Bias: Rediscovering Special Relativity

    Michael Lowry

    56

    PDF
  • Theory-Driven Discovery of Reaction Pathways in the MECHEM System

    Raúl E. Valdés-Perez

    63

    PDF
  • Discovery of Equations: Experimental Evaluation of Convergence

    Robert Zembowicz, Jan M. Zytkow

    70

    PDF
  • Operational Definition Refinement: A Discovery Process

    Jan M. Zytkow, Jieming Zhu, Robert Zembowicz

    76

    PDF

Learning: Inductive

  • Polynomial-Time Learning with Version Spaces

    Haym Hirsh

    117

    PDF
  • ChiMerge: Discretization of Numeric Attributes

    Randy Kerber

    123

    PDF
  • The Feature Selection Problem: Traditional Methods and a New Algorithm

    Kenji Kira, Larry A. Rendell

    129

    PDF
  • Discrete Sequence Prediction and its Applications

    Philip Laird

    135

    PDF
  • Classifier Learning from Noisy Data as Probabilistic Evidence Combination

    Steven W. Norton, Haym Hirsh

    141

    PDF
  • Sparse Data and the Effect of Overfitting Avoidance in Decision Tree Induction

    Cullen Schaffer

    147

    PDF
  • Complementary Discrimination Learning with Decision Lists

    Wei-Min Shen

    153

    PDF
  • Learning in FOL with a Similarity Measure

    Gilles Bisson

    82

    PDF
  • Learning to Learn Decision Trees

    Vlad G. Dabija, Katsuhiko Tsujino, Shogo Nishida

    88

    PDF
  • A Personal Learning Apprentice

    Lisa Dent, Jesus Boticario, Tom Mitchell, David Zabowski, John McDermott

    96

    PDF
  • The Attribute Selection Problem in Decision Tree Generation

    Usama M. Fayyad, Keki B. Irani

    104

    PDF
  • COGIN: Symbolic Induction with Genetic Algorithms

    David Perry Greene, Stephen F. Smith

    111

    PDF

Learning: Neural Network and Hybrid

  • A Framework for Integrating Fault Diagnosis and Incremental Knowledge Acquisition in Connectionist Expert Systems

    Joo-Hwee Lim, Ho-Chung Lui, Pei-Zhuang Wang

    159

    PDF
  • Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding

    Richard Maclin, Jude W. Shavlik

    165

    PDF
  • Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta

    Richard S. Sutton

    171

    PDF
  • Using Symbolic Learning to Improve Knowledge-Based Neural Networks

    Geoffrey G. Towell, Jude W. Shavlik

    177

    PDF

Learning: Robotic

  • Reinforcement Learning with Perceptual Aliasing: The Perceptual Distinctions Approach

    Lonnie Chrisman

    183

    PDF
  • Acquisition of Automatic Activity through Practice: Changes in Sensory Input

    Jack Gelfand, Marshall Flax, Raymond Endres, Stephen Lane, David Handelman

    189

    PDF
  • Automatic Programming of Robots Using Genetic Programming

    John R. Koza, James P. Rice

    194

    PDF
  • Reinforcement Learning with a Hierarchy of Abstract Models

    Satinder P. Singh

    202

    PDF

Learning: Theory

  • Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning

    Thomas Dean, Kenneth Basye, Leslie Kaelbling, Evangelos Kokkevis, Oded Maron, Dana Angluin, Sean Engelson

    208

    PDF
  • Oblivious PAC Learning of Concept Hierarchies

    Thomas Dean, Kenneth Basye, Leslie Kaelbling, Evangelos Kokkevis, Oded Maron, Dana Angluin, Sean Engelson

    215

    PDF
  • An Analysis of Bayesian Classifiers

    Pat Langley, Wayne Iba, Kevin Thompson

    223

    PDF
  • A Theory of Unsupervised Speedup Learning

    Prasad Tadepalli

    229

    PDF

Learning: Utility and Bias

  • A Statistical Approach to Solving the EBL Utility Problem

    Russell Greiner, Igor Jurisica

    241

    PDF
  • Empirical Analysis of the General Utility Problem in Machine Learning

    Lawrence B. Holder

    249

    PDF
  • Inductive Policy

    Foster John Provost, Bruce G. Buchanan

    255

    PDF
  • COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-Up Learning

    Jonathan Gratch, Gerald DeJong

    235

    PDF

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