Results 301 to 310 of about 1,373,879 (350)

A Classifier Ensemble Method for Fuzzy Classifiers

2006
In this paper, a classifier ensemble method based on fuzzy integral for fuzzy classifiers is proposed. The object of this method is to reduce subjective factor in building a fuzzy classifier, and to improve the classification recognition rate and stability for classification system.
Ai-Min Yang 0002, Yongmei Zhou, Min Tang 0003   +2 more
openaire   +1 more source

Bayes' Theorem and Naive Bayes Classifier

Encyclopedia of Bioinformatics and Computational Biology, 2019
The goal of this article is to give a mathematically rigorous yet easily accessible introduction to Bayes’ theorem and the foundations of naive Bayes learning. Starting from fundamental elements of probability theory, this text outlines all steps leading
D. Berrar
semanticscholar   +1 more source

Classifier Selection in a Family of Polyhedron Classifiers

2009
We consider an algorithm to approximate each class region by a small number of convex hulls and to apply them to classification. The convex hull of a finite set of points is computationally hard to be constructed in high dimensionality. Therefore, instead of the exact convex hull, we find an approximate convex hull (a polyhedron) in a time complexity ...
Tetsuji Takahashi, Mineichi Kudo, Atsuyoshi Nakamura   +2 more
openaire   +1 more source

Classifying groupware

Proceedings of the 38th annual on Southeast regional conference, 2000
The definition of what groupware is can be a topic of great debate and is often very broad. This allows many types of software to earn the name groupware but makes it very difficult to compare applications and to do any kind of background research in the field.
Jonathan D. Fouss, Kai-Hsiung Chang
openaire   +1 more source

On combining classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.
Josef Kittler, Mohamad Hatef, Robert P. W. Duin, Jiri Matas   +3 more
openaire   +1 more source

Dynamic base classifier pool for classifier selection in Multiple Classifier Systems

2011 International Conference on Machine Learning and Cybernetics, 2011
Multiple Classifier Systems (MCSs) are a method combining decisions of base classifiers. The set of the base classifiers is fixed in traditional MCSs. When applying MCSs in online learning environment, the base classifiers have to be updated frequently to adapt the change of the environment.
Patrick P. K. Chan, Qin-Qin Zhang, Wing W. Y. Ng, Daniel S. Yeung   +3 more
openaire   +1 more source

CCC: Classifier Combination via Classifier

2011
The combination of classifier has long been proposed as a method to improve the accuracy achieved in isolation by a single classifier. Most of the extant works focus on how to generate a group of "good" base classifiers, such as AdaBoost and Bagging. We are interested in the method of combining multiple classifiers.
openaire   +1 more source

Noun classifiers

2000
Abstract Noun classifiers occur with a noun independently of any other element within a noun phrase or a clause. They categorize the entity in terms of the generic type or class it belongs to. Noun classifiers can be independent words, as in Mayan, Australian, and some Austronesian and Amazonian languages, or they can be affixes to nouns
openaire   +1 more source

Multiple Classifier Systems

Lecture Notes in Computer Science, 2000
Thomas G. Dietterich
semanticscholar   +1 more source

Classified

Environmental Science & Technology, 1982
openaire   +2 more sources