Comparing data objects is at the heart of machine learning. For continuous data, object dissimilarity usually taken to be distance; however, for categorical there no universal agreement, categories can ordered in several different ways. Most existing category measures characterize distance among values an attribute may take using precisely number takes (the space) and frequency which they occur...

We propose a new similarity measure for interval-valued fuzzy sets and show its usefulness in medical diagnostic reasoning. We point out advantages of this new concept over the most commonly used similarity measures which are the counterparts of distances. The measure we propose involves both similarity and dissimilarity.

A conjecture of Mirkin, Muchnik, and Smith is answered affirmatively which connects the inconsistency function, a biologically meaningful similarity/dissimilarity measure for a gene tree and a species tree, to the mutation cost function, a combinatorial measure based on the mapping of trees. A linear-time algorithm for computing the mutation cost function is also derived from the conjecture.

2 Abstract. Ecological studies have now gone beyond measures of species turnover towards measures of phylogenetic and functional dissimilarity. This change of perspective has a main objective: disentangling the processes that drive species distributions from local to broad scales. A fundamental difference between phylogenetic and functional analyses is that phylogeny is intrinsically dependent ...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy OPTIMIZED DICTIONARY DESIGN AND CLASSIFICATION USING THE MATCHING PURSUITS DISSIMILARITY MEASURE By Raazia Mazhar May 2009 Chair: Paul D. Gader Co-chair: Joseph N. Wilson Major: Computer Engineering Discrimination-based classifiers diffe...

This paper computationalizes two linguistic concepts, contrast and focus, for the extraction of pragmatic and semantic salience from spontaneous speech. Contrast and focus have been widely investigated in modern linguistics, as categories that link intonation and information/discourse structure. This paper demonstrates the automatic tagging of contrast and focus for the purpose of robust sponta...

Current classification algorithms focus on vectorial data, given in euclidean or kernel spaces. Many real world data, like biological sequences are not vectorial and often non-euclidean, given by (dis-)similarities only, requesting for efficient and interpretable models. Current classifiers for such data require complex transformations and provide only crisp classification without any measure o...

In this paper, we establish that the following fitting problem is NP-hard: given a finite set X and a dissimilarity measure d on X (d is a symmetric function d from X to the nonnegative real numbers and vanishing on the diagonal), we wish to find a Robinsonian dissimilarity dR on X minimizing the l∞-error ||d− dR||∞ = maxx,y∈X{|d(x, y)− dR(x, y)|} between d and dR. Recall that a dissimilarity d...

We evaluate a version of the recently-proposed Optimized Dissimilarity Space Embedding (ODSE) classification system that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a labeled graph classification system. However, since it is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted ...