Selective Sampling for Nearest Neighbor Classifiers
نویسندگان
چکیده
منابع مشابه
{36 () Selective Sampling for Nearest Neighbor Classiiers *
Most existing inductive learning algorithms assume the availability of a training set of labeled examples. In many domains, however, labeling the examples is a costly process that requires either intensive computation or manual labor. In such cases, it may be beneecial for the learner to be active by intelligent selection of examples for labeling with the goal of reducing the labeling cost. In ...
متن کاملSelective Sampling for Nearest Neighbor Classi ers
In the passive, traditional, approach to learning, the information available to the learner is a set of classiied examples, which are randomly drawn from the instance space. In many applications, however, the initial clas-siication of the training set is a costly process, and an intelligently selection of training examples from unla-beled data is done by an active learner. This paper proposes a...
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The 1-N-N classifier is one of the oldest methods known. The idea is extremely simple: to classify X find its closest neighbor among the training points (call it X ,) and assign to X the label of X .
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We develop a probabilistic bound on the error rate of the nearest neighbor classiier formed from a set of labelled examples. The bound is computed using only the examples in the set. A subset of the examples is used as a validation set to bound the error rate of the classiier formed from the remaining examples. Then a bound is computed for the diierence in error rates between the original class...
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This paper presents experiments of Nearest Neighbor (NN) classifier design using different evolutionary computation methods. Through multi-objective and co-evolution techniques, it combines genetic algorithms and genetic programming to both select NN prototypes and design a neighborhood proximity measure, in order to produce a more efficient and robust classifier. The proposed approach is compa...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2004
ISSN: 0885-6125
DOI: 10.1023/b:mach.0000011805.60520.fe