Solution Path Algorithm for Twin Multi-class Support Vector Machine

The twin support vector machine and its extensions have made great achievements in dealing with binary classification problems, however, which is faced with some difficulties such as model selection and solving multi-classification problems quickly. This paper is devoted to the fast regularization parameter tuning algorithm for the twin multi-class support vector machine. A new sample dataset division method is adopted and the Lagrangian multipliers are proved to be piecewise linear with respect to the regularization parameters by combining the linear equations and block matrix theory. Eight kinds of events are defined to seek for the starting event and then the solution path algorithm is designed, which greatly reduces the computational cost. In addition, only few points are combined to complete the initialization and Lagrangian multipliers are proved to be 1 as the regularization parameter tends to infinity. Simulation results based on UCI datasets show that the proposed method can achieve good classification performance with reducing the computational cost of grid search method from exponential level to the constant level

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Exploring QSARs for Inhibitory Activity of Non-peptide HIV-1 Protease Inhibitors by GA-PLS and GA-SVM

The support vector machine (SVM) and partial least square (PLS) methods were used to develop quantitative structure activity relationship (QSAR) models to predict the inhibitory activity of non-peptide HIV-1 protease inhibitors. Genetic algorithm (GA) was employed to select variables that lead to the best-fitted models. A comparison between the obtained results using SVM with those of PLS revealed that the SVM model is much better than that of PLS. The root mean square errors of the training set and the test set for SVM model were calculated to be 0.2027, 0.2751, and the coefficients of determination (R(2)) are 0.9800, 0.9355 respectively. Furthermore, the obtained statistical parameter of leave-one-out cross-validation test (Q(2)) on SVM model was 0.9672, which proves the reliability of this model. The results suggest that TE2, Ui, GATS5e, Mor13e, ATS7m, Ss, Mor27e, and RDF035e are the main independent factors contributing to the inhibitory activities of the studied compounds.The authors would like to acknowledge the computational chemistry laboratory at Al-Quds University for providing Matlab software and for the time dedicated for performing the calculations of the study

Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines

In many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for quality assessment (QA) in manufacturing industries. In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set. Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided

Facial expression recognition in the wild : from individual to group

The progress in computing technology has increased the demand for smart systems capable of understanding human affect and emotional manifestations. One of the crucial factors in designing systems equipped with such intelligence is to have accurate automatic Facial Expression Recognition (FER) methods. In computer vision, automatic facial expression analysis is an active field of research for over two decades now. However, there are still a lot of questions unanswered. The research presented in this thesis attempts to address some of the key issues of FER in challenging conditions mentioned as follows: 1) creating a facial expressions database representing real-world conditions; 2) devising Head Pose Normalisation (HPN) methods which are independent of facial parts location; 3) creating automatic methods for the analysis of mood of group of people. The central hypothesis of the thesis is that extracting close to real-world data from movies and performing facial expression analysis on movies is a stepping stone in the direction of moving the analysis of faces towards real-world, unconstrained condition. A temporal facial expressions database, Acted Facial Expressions in the Wild (AFEW) is proposed. The database is constructed and labelled using a semi-automatic process based on closed caption subtitle based keyword search. Currently, AFEW is the largest facial expressions database representing challenging conditions available to the research community. For providing a common platform to researchers in order to evaluate and extend their state-of-the-art FER methods, the first Emotion Recognition in the Wild (EmotiW) challenge based on AFEW is proposed. An image-only based facial expressions database Static Facial Expressions In The Wild (SFEW) extracted from AFEW is proposed. Furthermore, the thesis focuses on HPN for real-world images. Earlier methods were based on fiducial points. However, as fiducial points detection is an open problem for real-world images, HPN can be error-prone. A HPN method based on response maps generated from part-detectors is proposed. The proposed shape-constrained method does not require fiducial points and head pose information, which makes it suitable for real-world images. Data from movies and the internet, representing real-world conditions poses another major challenge of the presence of multiple subjects to the research community. This defines another focus of this thesis where a novel approach for modeling the perception of mood of a group of people in an image is presented. A new database is constructed from Flickr based on keywords related to social events. Three models are proposed: averaging based Group Expression Model (GEM), Weighted Group Expression Model (GEM_w) and Augmented Group Expression Model (GEM_LDA). GEM_w is based on social contextual attributes, which are used as weights on each person's contribution towards the overall group's mood. Further, GEM_LDA is based on topic model and feature augmentation. The proposed framework is applied to applications of group candid shot selection and event summarisation. The application of Structural SIMilarity (SSIM) index metric is explored for finding similar facial expressions. The proposed framework is applied to the problem of creating image albums based on facial expressions, finding corresponding expressions for training facial performance transfer algorithms

Visual Tracking by Sampling in Part Space

In this paper, we present a novel part-based visual tracking method from the perspective of probability sampling. Specifically, we represent the target by a part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines whether to accept its vote or to reject it. By doing this, we transform the complex online part selection problem into a probability learning one, which is easier to tackle. The observation model of each part is constructed by an improved supervised descent method and is learned in an incremental manner. Experimental results on two benchmarks demonstrate the competitive performance of our tracker against state-of-the-art methods

An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods

Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients’ activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric.publishedVersio