The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vec...

Our goal is to improve the training and prediction time of Nyström method, which is a widely-used technique for generating low-rank kernel matrix approximations. When applying the Nyström approximation for large-scale applications, both training and prediction time is dominated by computing kernel values between a data point and all landmark points. With m landmark points, this computation requ...

Recently, many studies based on microRNAs (miRNAs) showed a new aspect of cancer classification, and feature selection methods are used to reduce the high dimensionality of miRNA expression data. These methods just consider the problem of where feature to class is 1:1 or n:1. But one miRNA may have influence to more than one type of cancers. However, these miRNAs are considered to be low ranked...

Machine learning algorithms have been investigated in several scenarios, one of them is the data classification. The predictive performance of the models induced by these algorithms is usually strongly affected by the values used for their hyper-parameters. Different approaches to define these values have been proposed, like the use of default values and optimization techniques. Although defaul...

This year EURECOM participated in the TRECVID 2013 Semantic INdexing (SIN) Task [11] for the submission of four different runs for 60 concepts. Our submission builds on the runs submitted last year at the 2012 SIN task, the details of which can be found in [8]. In 2013, two runs are combinations of basic descriptors. One run adds uploaders bias to the pool of visual features while another run w...

We examine a new form of smooth approximation to the zero one loss in which learning is performed using a reformulation of the widely used logistic function. Our approach is based on using the posterior mean of a novel generalized BetaBernoulli formulation. This leads to a generalized logistic function that approximates the zero one loss, but retains a probabilistic formulation conferring a num...

Identification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especially classification. In order to achieve successful classification, many parameters need to be considered such as data quality, choice of classifie...

Selection of feature subset is a preprocessing step in computational learning, and it serves several purposes like reducing the dimensionality of a dataset, decreasing the computational time required for classification and enhancing the classification accuracy of a classifier by removing redundant and misleading or erroneous features. This paper presents a new feature selection and weighting me...

We present an optimization framework for graph-regularized multi-task SVMs based on the primal formulation of the problem. Previous approaches employ a so-called multi-task kernel (MTK) and thus are inapplicable when the numbers of training examples n is large (typically n < 20, 000, even for just a few tasks). In this paper, we present a primal optimization criterion, allowing for general loss...

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in diffe...