The common problems in machine learning from omics data are the scarcity of samples, the high number of features and their complex interaction structure. The models built solely from measured data often suffer from overfitting. One of possible methods dealing with overfitting is to use prior knowledge for regularization. This work analyzes contribution of feature interaction networks in regular...

This paper accelerates boosted nonlinear weak classifiers in boosting framework for object detection. Although conventional nonlinear classifiers are usually more powerful than linear ones, few existing methods integrate them into boosting framework as weak classifiers owing to the highly computational cost. To address this problem, this paper proposes a novel nonlinear weak classifier named Pa...

This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection. Instead of using exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two methods based on PSO. The first uses PSO to evolve and select good features only and the weak classifiers use a simple decision stump....

In this paper, we study the use of boosted weak classifiers selected with AdaBoost algorithm in object detection. Our work is motivated by the good performance of AdaBoost in selecting discriminative features and the effectiveness of Classification and Regression Tree (CART) compared with other classification methods. First, we study the cascaded structure of the boosted weak classifier detecto...

Abstract We prove a correspondence between $\kappa$ -small fibrations in simplicial presheaf categories equipped with the injective or projective model structure (and left Bousfield localizations thereof) and relatively -compact maps their underlying quasi-categories for suitably large regular cardinals . thus obtain transition result weakly universal small (type-theoretic) Dugger–Rezk-style st...

We present a two-class pattern recognition method through the majority vote which is based on weak classifiers. The weak classifiers are defined‘in terms of rectangular regions formed by the original training data. Tests on real and simulated data sets show that this classifier combination procedure can lead to a high accuracy.

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...

This paper presents an investigation into the classification of a difficult data set containing large intra-class variability but low inter-class variability. Standard classifiers are weak and fail to achieve satisfactory results however, it is proposed that a combination of such weak classifiers can improve overall performance. The paper also introduces a novel evolutionary approach to fuzzy r...