Titlebook: Machine Learning and Data Mining in Pattern Recognition; 8th International Co Petra Perner Conference proceedings 2012 Springer-Verlag Berl

准则

Constructing Target Concept in Multiple Instance Learning Using Maximum Partial Entropynces. In this paper, we advance the problem with a novel method based on computing the partial entropy involving only the positive bags using a partial probability scheme in the attribute subspace. The evaluation highlights what could be obtained if information only from the positive bags is used, w

填满

灵敏

旧石器

A New Learning Strategy of General BAMs the ability to recall a stored pattern from a noisy input, which depends on learning process. Between two learning types of iterative learning and non-iterative learning, the former allows better noise tolerance than the latter. However, interactive learning BAMs take longer to learn. In this paper

Tortuous

Proximity-Graph Instance-Based Learning, Support Vector Machines, and High Dimensionality: An Empiriing algorithms for pattern classification applications. However, as the dimensionality of the data grows large, all data points in the training set tend to become Gabriel neighbors of each other, bringing the efficacy of this method into question. Indeed, it has been conjectured that for high-dimens

药物

Semi Supervised Clustering: A Pareto Approach of supervised data known as constraints, to assist unsupervised learning. Instead of modifying the clustering objective function, we add another objective function to satisfy specified constraints. We use a lexicographically ordered cluster assignment step to direct the search and a Pareto based mu

阐释

翻布寻找

大方不好

978-3-642-31536-7Springer-Verlag Berlin Heidelberg 2012

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