Citations, abstracts and cited references from more than 5,700 journals in 164 scientific disciplines. This version covers 2,100 more journals than the print or CD-ROM version, with all cited references captured.

In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set

We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing

In this introductory paper an attempt is made to give an overview of the area of nanostructured „materials irrespective of the synthesis process. The various microstructural features such as clusters or isolated nanoparticles, agglomerated nanopowders, consolidated nanomaterials and nanocomposite

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We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features

This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI

Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve

develop a systematic scheme that more fully demarcates the unique features, functions, and sources of embeddedness. From this scheme, I derive a set of refutable implications and test their plausibility, using another data set on the network ties of all better dress apparel firms in the New York apparel