Good similarity functions are at the heart of effective case-based reasoning. However, the similarity functions that have been designed so far have been mostly linear, weighted-sum in nature. In this paper, we explore how to handle case retrieval when the case base is nonlinear in similarity measurement, in which situation the linear similarity functions will result in the wrong solutions. Our ...

This paper presents a morphological scale-space approach to the problem of feature extraction. The method relies on two steps: a hierarchical simplification step based on pyramids of morphological operators and a feature extraction step consisting in measuring the persistence of each image structure through the simplification scales. Specific scalespace properties are needed: the features shoul...

Full covariance models can give better results for speech recognition than diagonal models, yet they introduce complications for standard speaker adaptation techniques such as MLLR and fMLLR. Here we introduce efficient update methods to train adaptation matrices for the full covariance case. We also experiment with a simplified technique in which we pretend that the full covariance Gaussians a...

Specifying an appropriate feature space is an important aspect of achieving good performance when designing systems based upon learned classifiers. Effectively incorporating information regarding semantically related words into the feature space is known to produce robust, accurate classifiers and is one apparent motivation for efforts to automatically generate such resources. However, naive in...

This paper presents a multiple templates matching algorithm based on feature space trace, which is used in speaker recognition. It extracts the cepstrum coefficient as feature parameter. We normalize the sequence of feature parameter based on feature space trace. The fuzzy c-means method is adopted in generating the multiple templates and the multiple matching method is applied to match the tem...

In this paper, we continue our study of learning the kernel. We present a reformulation of this problem within a feature space environment. This leads us to study regularization in the dual space of all continuous functions on a compact domain with values in a Hilbert space with a mix norm. We also relate this problem in a special case to regularization. 1This work was supported by NSF Grant IT...

The transductive SVM is a semi-supervised learning algorithm that searches for a large margin hyperplane in feature space. By withholding the training labels and adding a constraint that favors balanced clusters, it can be turned into a clustering algorithm. The Normalized Cuts clustering algorithm of Shi and Malik, although originally presented as spectral relaxation of a graph cut problem, ca...

A classical problem in many computer graphics applications consists in extracting significant zones or points on an object surface, like loci of tangent discontinuity (edges), maxima or minima of curvatures, inflection points, etc. These places have specific local geometrical properties and often called generically features. An important problem is related to the scale, or range of scales, for ...

We show that the relevant information about a classification problem in feature space is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matches the underlying learning problem. Thus, kernels not only transform data sets such that good generalization can be achieved even by linear discriminant functions, but this transformation is also performe...

The Author Identification task for PAN 2016 consisted of three different Sub-tasks: authorship clustering, authorship links and author diarization. We developed a machine learning approaches for two of three of these tasks. For the two authorship related tasks we created various sets of feature spaces. The challenge was to combine these feature spaces to enable the machine learning algorithms t...