Some studies in machine learning using the game of checkers
Computers & thought
A General Framework for Induction and a Study of Selective Induction
Machine Learning
Feature Selection for Meta-learning
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Information-Theoretic Measures for Meta-learning
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Adapting bias by gradient descent: an incremental version of delta-bar-delta
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
Automatic selection of classification learning algorithms for data mining practitioners
Intelligent Data Analysis
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Concept learning is inherently complex. Without severe constraint or inductive "bias," the general problem is intractable. While most learning systems have been designed with built-in biases, these systems typically work well only in narrowly circumscribed problem domains. Here we present a model of concept formation that views learning as a simultaneous optimization problem at three different levels, with dynamically chosen biases guiding the search for satisfactory hypotheses. In this model, the partitioning of events into classes occurs through dynamic interactions among three layers: event space, hypothesis space, and bias space. This view of the induction process may help clarify the problem of learning and lead to more general and efficient induction systems. To test this model of meta-knowledge, a variable bias management system (VBMS) has been designed and partly implemented. The system will dynamically alter evolving hypotheses, concept representation languages, and concept formation algorithms by monitoring progress and selecting biases based on characteristics of the particular induction problems presented. VBMS is designed to learn the best biases for different types of induction problems. Thus it is robust (effective and efficient in many domains). The system can learn incrementally despite noisy data at any level.