The protein structural class is considered as a multi-class classification. The feature of protein structure was extracted from the protein convex hull which was occupied from the geometrical approximation of protein structure. This multi-class classification is solved by Multi-NSVR(-S), which is constructed by our algorithm-NSVR. To overcome the difficulty of class imbalance in the data, the m...

Recently, logical analysis of data (LAD) using a classifier based on a linear combination of patterns has been introduced, providing high classification accuracy and pattern-based interpretability on classification results. However, it is known that most of LAD-based multi-classification algorithms have conflicts between classification accuracy and computational complexity because they are base...

Fold recognition based on sequence-derived features is a complex multi-class classification problem. In the current study, we comparatively assess five different classification techniques, namely multilayer perceptron and probabilistic neural networks, nearest neighbour classifiers, multi-class support vector machines and classification trees for fold recognition on a reference set of proteins ...

We present specializing, a method for combining classifiers for multi-class classification. Specializing trains one specialist classifier per class and utilizes each specialist to distinguish that class from all others in a one-versus-all manner. It then supplements the specialist classifiers with a catch-all classifier that performs multi-class classification across all classes. We refer to th...

Semi-supervised learning, which aims to learn from partially labeled data and mostly unlabeled data, has been attracted more and more attention in machine learning and pattern recognition. A novel semi-supervised classification approach is proposed, which can not only handle semi-supervised binary classification problem but also deal with semi-supervised multi-class classification problem. The ...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.03.023 q This project was supported by CONACyT unde scholarship 205834. ⇑ Corresponding author. Tel.: +52 2222663100x831 E-mail address: [email protected] (H.J. Escalant This article describes the application of particle swarm model selection (PSMS) to the problem of automatic image annotation (AIA). PSMS c...

BACKGROUND Multi-class molecular cancer classification has great potential clinical implications. Such applications require statistical methods to accurately classify cancer types with a small subset of genes from thousands of genes in the data. OBJECTIVES This paper presents a new functional gradient descent boosting algorithm that directly extends the HingeBoost algorithm from the binary ca...

LIght Detection And Ranging (LIDAR) is a powerful remote sensing technology in the acquisition of the terrain surface information for object classification and extraction. Major benefits of this technique are its high level of automation during data capturing and its spatial resolution. Because of high complexities and difficulties in urban areas, many researchers focus on the using of LIDAR da...

Steinwart was the first to prove universal consistency of support vector machine classification. His proof analyzed the ‘standard’ support vector machine classifier, which is restricted to binary classification problems. In contrast, recent analysis has resulted in the common belief that several extensions of SVM classification to more than two classes are inconsistent. Countering this belief, ...

The goal of multi-image classification is to identify and label "similar regions" within a scene. The ability to correctly classify a remotely sensed multi-image of a scene is affected by the ability of the classification process to adequately compensate for the effects of atmospheric variations and sensor anomalies. Better classification may be obtained if the multi-image is preprocessed befor...