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  • C4.5: Programs for Machine Learning

    J. Ross Quinlan

    Morgan Kaufmann, 1993

    Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged ...

    Cited by 831 References 0
    bibtex
  • Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations

    Ian H. Witten, Eibe Frank

    Morgan Kaufmann, 1999

    Cited by 347 References 0
    bibtex
  • Support Vector Machine Active Learning with Application sto Text Classification

    Simon Tong, Daphne Koller

    17th International Conference on Machine Learning, 2000  –  ICML

    Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they ...

    Cited by 50 References 0
    bibtex
  • Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems

    Sholom M. Weiss, Casimir A. Kulikowski

    Morgan Kaufmann, 1990

    Cited by 32 References 0
    bibtex
  • Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

    Mark A. Hall

    17th International Conference on Machine Learning, 2000  –  ICML

    Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of ...

    Cited by 27 References 0
    bibtex
  • A Machine Learning Approach to Building Domain-Specific Search Engines

    Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore

    Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 99, 1999  –  IJCAI

    Domain-specific search engines are becoming increasingly popular because they offer increased accuracy and extra features not possible with general, ...

    Cited by 17 References 3
    bibtex
  • Elements of Machine Learning

    Pat Langley

    Morgan Kaufmann, 1994

    Cited by 16 References 0
    bibtex
  • A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering

    Pedro Domingos, Geoff Hulten

    18th International Conference on Machine Learning, 2001  –  ICML

    We propose to scale learning algorithms to arbitrarily large databases by the following method. First derive an upper bound for the learner's ...

    Cited by 14 References 0
    bibtex
  • Machine Learning: A Theoretical Approach

    Balas K. Natarajan

    Morgan Kaufmann, 1991

    Cited by 3 References 0
    bibtex
  • Structural Machine Learning with Galois Lattice and Graphs

    Michel Liquiere, Jean Sallantin

    15th International Conference on Machine Learning, 1998  –  ICML

    This paper defines a formal approach to learning from examples described by labelled graphs. We propose a formal model based upon lattice theory and ...

    Cited by 2 References 0
    bibtex
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