of stimulus and response generalization in learning, it is employed to explain errors in memory and pattern recognition, and it is central to the analysis of connotative meaning. Similarity or dissimilarity data appear in di¤erent forms: ratings of pairs, sorting of objects, communality between associations

Words are important both in historical linguistics and natural language processing. They are not indivisible abstract atoms; much can be gained by considering smaller units such as morphemes, phonemes, syllables, and letters. In this presentation, I attempt to sketch the similarity patterns among a

greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed

ABSTRACT A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering

In this paper, we describe the process of discovering underlying knowledge in a set of isokinetic tests, using a new algorithm to find similar patterns in a set of temporal series. An isokinetic machine is basically a physical support on which patients exercise one of their joints, in this case

in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multi-condition gene expression patterns. In this paper we describe a novel clustering algorithm that was developed for analysis of gene

and in psychiatric settings. It was found to have very high internal consistency and adequate test-retest repeatability. Validity was established by pat-terns of correlations with other self-report measures, by correlations with clinical ratings of depression, and by relationships with other variables which support

Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such “exemplars ” can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only

The paper presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps

placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission’s range is optimally chosen, the bit–distance product that can be transported by the network per second is 2 @ A bit-meters per second. Thus even under optimal circumstances, the throughput is only 2 bits per