Communications of the ACM - Special issue on parallelism
On learning the past tenses of English verbs
Parallel distributed processing: explorations in the microstructure of cognition, vol. 2
Instance-Based Learning Algorithms
Machine Learning
Experimental and theoretical artificial intelligence
Journal of Experimental & Theoretical Artificial Intelligence
Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
A study of instance-based algorithms for supervised learning tasks: mathematical, empirical, and psychological evaluations
Combining the Strength of Pattern Frequency and Distance for Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Random Case Analysis of Inductive Learning Algorithms
DS '98 Proceedings of the First International Conference on Discovery Science
Dimensionality Reduction through Sub-space Mapping for Nearest Neighbor Algorithms
ECML '00 Proceedings of the 11th European Conference on Machine Learning
A decision rule-based method for feature selection in predictive data mining
Expert Systems with Applications: An International Journal
Focusing solutions for data mining: analytical studies and experimental results in real-world domains
An ensemble of filters and classifiers for microarray data classification
Pattern Recognition
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Combination of multiple nearest neighbor classifiers based on feature subset clustering method
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Automated user modeling for personalized digital libraries
International Journal of Information Management: The Journal for Information Professionals
A new matching strategy for content based image retrieval system
Applied Soft Computing
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In this paper we present an average-case analysis of the nearest neighbor algorithm, a simple induction method that has been studied by many researchers. Our analysis assumes a conjunctive target concept, noise-free Boolean attributes, and a uniform distribution over the instance space. We calculate the probability that the algorithm will encounter a test instance that is distance d from the prototype of the concept, along with the probability that the nearest stored training case is distance e from this test instance. From this we compute the probability of correct classification as a function of the number of observed training cases, the number of relevant attributes, and the number of irrelevant attributes. We also explore the behavioral implications of the analysis by presenting predicted learning curves for artificial domains, and give experimental results on these domains as a check on our reasoning.