The machine learning problem of classifier design is studied from the perspective of probability elicitation, in statistics. This shows that the standard approach of proceeding from the specification of a loss, to the minimization of conditional risk is overly restrictive. It is shown that a better alternative is to start from the specification of a functional form for the minimum conditional r...

Regularization is commonly used in classifier design, to assure good generalization. Classical regularization enforces a cost on classifier complexity, by constraining parameters. This is usually combined with a margin loss, which favors large-margin decision rules. A novel and unified view of this architecture is proposed, by showing that margin losses act as regularizers of posterior class pr...

The recently proposed “generalized min-max” (GMM) kernel [9] can be efficiently linearized, with direct applications in large-scale statistical learning and fast near neighbor search. The linearized GMM kernel was extensively compared in [9] with linearized radial basis function (RBF) kernel. On a large number of classification tasks, the tuning-free GMM kernel performs (surprisingly) well comp...

The problem of controlling the margin of a classifier is studied. A detailed analytical study is presented on how properties of the classification risk, such as its optimal link and minimum risk functions, are related to the shape of the loss, and its margin enforcing properties. It is shown that for a class of risks, denoted canonical risks, asymptotic Bayes consistency is compatible with simp...

Heart disease is one of the most common causes death globally. In this study, machine learning algorithms and models widely used in literature to predict heart have been extensively compared, a hybrid feature selection based on genetic algorithm tabu search methods developed. The proposed system consists three components: (1) preprocess datasets, (2) with algorithm, (3) classification module. t...

Graphics recognition for raster-based input discovers primitives such as lines, arrowheads, and circles. This paper focuses on graphics recognition of figures in vector-based PDF documents. The first stage consists of extracting the graphic and text primitives corresponding to figures. An interpreter was constructed to translate PDF content into a set of self-contained graphics and text objects...

From family of corrective boosting algorithms (i.e. AdaBoost, LogitBoost) to total corrective algorithms (i.e. LPBoost, TotalBoost, SoftBoost, ERLPBoost), we analysis these methods of sample weight updating. Corrective boosting algorithms update the sample weight according to the last hypothesis; comparatively, total corrective algorithms update the weight with the best one of all weak classifi...

Many works address the problem of object detection by means of machine learning with boosted classifiers. They exploit sliding window search, spanning the whole image: the patches, at all possible positions and sizes, are sent to the classifier. Several methods have been proposed to speed up the search (adding complementary features or using specialized hardware). In this paper we propose a sta...

We present a fast method to detect humans from stationary surveillance videos. It is based on a cascade of LogitBoost classifiers which use covariance matrices as object descriptors. We have made several contributions. First, our method learns the correlation between appearance and foreground features and show that the human shape information contained in foreground observations can dramaticall...

Video object segmentation has been widely used in many fields. A conditional random fields (CRF) model is proposed to achieve accurate multi-class segmentation of video objects in the complex environment. By using CRF, the color, texture, motion characteristics and neighborhood relations of objects are modeled to construct the corresponding energy functions in both the temporal and spatial doma...