Searching and organizing growing digital music collections requires automatic classification of music. This paper describes a new system, tested on the task of artist identification, that uses support vector machines to classify songs based on features calculated over their entire lengths. Since support vector machines are exemplarbased classifiers, training on and classifying entire songs inst...

This work focuses on detecting outliers within large and very large datasets using a computationally efficient procedure. The algorithm uses Tukey’s biweight function applied on the dataset to filter out the effects of extreme values for obtaining appropriate location and scale estimates. Robust Mahalanobis distances for all data points are calculated using these location and scale estimates. A...

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...

In this paper we consider the performance of the widely adopted K-means clustering algorithm when the classification variables are correlated. We measure performance in terms of recovery of the true data structure. As expected, performance worsens considerably if the groups have elliptical instead of spherical shape. We suggest some modifications to the standard K-means algorithm which consider...

The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed BOOSTMETRIC, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used t...

In this paper a new learning algorithm is proposed with the purpose of texture segmentation. The algorithm is a competitive clustering scheme with two specific features: elliptical clustering is accomplished by incorporating the Mahalanobis distance measure into the learning rules, and underutilization of smaller clusters is avoided by incorporating a frequency-sensitive term. In the paper, an ...

In software development, comprehensive software reviews and testings are important activities to preserve high quality and to control maintenance cost. However it would be actually difficult to perform comprehensive software reviews and testings because of a lot of components, a lack of manpower and other realistic restrictions. To improve performances of reviews and testings in object-oriented...

In the classical Gaussian SVM classification we use the feature space projection transforming points to normal distributions with fixed covariance matrices (identity in the standard RBF and the covariance of the whole dataset in Mahalanobis RBF). In this paper we add additional information to Gaussian SVM by considering local geometry-dependent feature space projection. We emphasize that our ap...

We present a novel approach to text categorisation with the aid of the Mahalanobis distance measure for classification. For correlated datasets, classification using the Euclidean distance is not very accurate. The use of the Mahalanobis distance exploits the correlation in data for the purpose of classification. For achieving this on large datasets, an unsupervised dimensionality reduction tec...

In order to provide ubiquitous access for the users, future generation network integrate a multitude of radio access technologies (RAT’S) which can interoperate between them. However, the most challenging problem is the selection of an optimal radio access network, in terms of quality of service anywhere at anytime. This paper proposes a novel ranking algorithm, which combines multi attribute d...