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An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
Abstract

Cited by 333 (17 self)
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In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that the algorithm will induce an arbitrary pair of concept descriptions and then use this to compute the probability of correct classification over the instance space. The analysis takes into account the number of training instances, the number of attributes, the distribution of these attributes, and the level of class noise. We also explore the behavioral implications of the analysis by presenting
Iterative Optimization and Simplification of Hierarchical Clusterings
 Journal of Artificial Intelligence Research
, 1995
"... Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high qual ..."
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Cited by 103 (1 self)
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Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a `tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modifies an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been construct...
Learning with probabilistic representations
 Machine Learning
, 1997
"... Machine learning cannot occur without some means to represent the learned knowledge. Researchers have long recognized the influence of representational choices, and the major paradigms in machine learning are organized not around induction algorithms or performance elements as much as around represe ..."
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Cited by 2 (1 self)
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Machine learning cannot occur without some means to represent the learned knowledge. Researchers have long recognized the influence of representational choices, and the major paradigms in machine learning are organized not around induction algorithms or performance elements as much as around representational classes. Major examples include logical
7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)
, 1991
"... j_ REPORT DOCUMENTATION PAGE OMBNo.07o4o188 Oijt31!C _e_3rt,r1 _ burden +c,r this, oile(tlOtl]f,nformatlOn,s estimated to average 1 hour per resooqse irlcIu_4_g rife t_me tor re_e'hmg irlstruGIOr;$, sear(rang e,rsDng dat _ sour¢_., _ather_r _:_r_dna!_t_l_r_g the _a[a needed. 3nd Como/etlng 3nO revi ..."
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