Explore the vast range of titles available.

\"Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors\"-- Provided by publisher.

Aiming at the problem that the most existing fault diagnosis methods could not effectively recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy information entropy, improved particle swarm optimization algorithm and least squares support vector machines are introduced into the fault diagnosis to propose a novel intelligent diagnosis method, which is applied to diagnose the faults of the motor bearing in this paper. In the proposed method, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by using empirical mode decomposition method. The fuzzy information entropy values of IMFs are calculated to reveal the intrinsic characteristics of the vibration signal and considered as feature vectors. Then the diversity mutation strategy, neighborhood mutation strategy, learning factor strategy and inertia weight strategy for basic particle swarm optimization (PSO) algorithm are used to propose an improved PSO algorithm. The improved PSO algorithm is used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier, which is used to classify the fault. Finally, the proposed fault diagnosis method is fully evaluated by experiments and comparative studies for motor bearing. The experiment results indicate that the fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods in this paper and published in the literature. It provides a new method for fault diagnosis of rotating machinery.

Twin support vector machines (TWSVM), a novel machine learning algorithm developing from traditional support vector machines (SVM), is one of the typical nonparallel support vector machines. Since the TWSVM has superiorities of the simple model, the high training speed and the good performance, it has drawn extensive attention. The initial TWSVM can only handle binary classification, however, the multi-class classification problems are also common in practice. How to extend TWSVM from binary classification to multi-class classification is an interesting issue. Many researchers have devoted to the study of multi-class TWSVM. Although the study of multi-class TWSVM has made great progress, there is little literature on the comparisons and summaries of different multi-class classifiers based on TWSVM, which not only makes it difficult for novices to understand the essential differences, but also leads to the problem that how to choose the suitable multi-class TWSVM for a practical multi-class classification problem. This paper aims to review the development of multi-class TWSVM in recent years. We group multi-classTWSVM reasonably and analyze them with the respect to the basic theories and geometric meaning. According to the structures of the multi-class TWSVM, we divide them to the following groups: “one-versus-rest” strategy based multi-classTWSVM, “one-versus-one” strategy based multi-class TWSVM, binary tree structure based multi-class TWSVM, “one-versus-one-versus-rest” strategy based multi-class TWSVM and “all-versus-one” strategy based multi-class TWSVM. Although the training processes of direct acyclic graph based multi-class TWSVM are much similar to that of “one-versus-one” multi-class TWSVM, the decision processes of direct acyclic graph based multi-class TWSVM have their own characteristics and disadvantages, so we divide them to a separate group. This paper analyzes and summarizes the basic thoughts, theories, applicability and complexities of different multi-class TWSVM of different groups and presents experimental results to compare the performances.

Twin support vector machine (TWSVM) has gained increasing interest from various research fields recently. In this paper, we aim to report the current state of the theoretical research and practical advances on TWSVM. We first give the basic thought and theory of TWSVM, including the theory of proximal support vector machine, generalized eigenvalue proximal support vector machine, and TWSVM. Then, we focus on the various improvements made to TWSVM, mainly including least squares twin support vector machine, smooth twin support vector machine, regularized twin support vector machine, projection twin support vector machine, and modified TWSVM on the model selection problem. These newly emerging algorithms greatly expand the applications of TWSVM. In recent years, there is a lot of research on application of TWSVM. Next, we list some research on application of TWSVM in detail. Finally, we try to provide a comprehensive view of these advances in TWSVM and discuss the direction of future research.

Support vector machines (SVMs) are a popular class of supervised learning algorithms, and are particularly applicable to large and high-dimensional classification problems. Like most machine learning methods for data classification and information retrieval, they require manually labeled data samples in the training stage. However, manual labeling is a time consuming and errorprone task. One possible solution to this issue is to exploit the large number of unlabeled samples that are easily accessible via the internet. This paper presents a novel active learning method for text categorization. The main objective of active learning is to reduce the labeling effort, without compromising the accuracy of classification, by intelligently selecting which samples should be labeled. The proposed method selects a batch of informative samples using the posterior probabilities provided by a set of multi-class SVM classifiers, and these samples are then manually labeled by an expert. Experimental results indicate that the proposed active learning method significantly reduces the labeling effort, while simultaneously enhancing the classification accuracy.

Security solutions in the cyber world are essential for enforcing protection against network vulnerabilities and data exploitation. Unauthorized access or attack can be avoided in critical systems using a comprehensive approach via an effective intrusion detection system (IDS). Traditional intrusion detection techniques are no longer accurate and effective enough to handle the demands of the big data age. Machine learning (ML) methods can be utilized for intrusion detection since the classifier’s performance has significantly increased over the past decade. A significant limitation of most ML-based IDSs is that they often generate alerts for false predictions. This is owing to misclassifications that tend to occur more frequently than actual threats. Despite their effectiveness, these conventional ML-based IDSs often face difficulties scaling to meet the demands of big data. The increasing volume and complexity of datasets pose various challenges, such as high dimensionality, multiple data sources, and the need for a dependable infrastructure. Consequently, the accuracy of an ML model likely declines when irrelevant features are included from a vast dataset. In this paper, the exhaustive feature selection algorithm is employed to assess every possible combination of features in a dataset to evaluate its performance. The selection is based on identifying the feature subset with the highest accuracy. Hence, an ML-based complete security solution is introduced for network intrusion detection using the supervised framework. This framework utilizes quantum-inspired least square support vector machine (LS-SVM) classifier. This algorithm is used to enhance the classification accuracy in terms of reducing false predictions while minimizing the training time. The hyperparameters of our model are tuned by utilizing those selected features to maximize the accuracy. The model developed is verified using three different datasets, which have been widely applied to intrusion detection. The model achieves high detection performance, with accuracy values of 99.3% for NSL-KDD, 99.5% for CIC-IDS-2017, and 93.3% for UNSW-NB15. Precision remains at 1.00 for CIC-IDS-2017 and UNSW-NB15, while recall reaches 1.00 for CIC-IDS-2017, 0.99 for NSL-KDD, and 0.98 for UNSW-NB15. F1-scores follow the same trend, reflecting the classifier’s robust prediction capabilities. In addition, our model demonstrates competitive testing time efficiency in 2.8 s for NSL-KDD, 1.0s for CIC-IDS-2017, and 2.8s for UNSW-NB15. Also, our model requires the minimum training time for all datasets compared to other models. These results highlight the LS-SVM-based model’s suitability for real-time intrusion detection applications.

Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.

Bearing defects have been accepted as one of the major causes of failure in rotating machinery. It is important to identify and diagnose the failure behavior of bearings for the reliable operation of equipment. In this paper, a low-cost non-contact vibration sensor has been developed for detecting the faults in bearings. The supervised learning method, support vector machine (SVM), has been employed as a tool to validate the effectiveness of the developed sensor. Experimental vibration data collected for different bearing defects under various loading and running conditions have been analyzed to develop a system for diagnosing the faults for machine health monitoring. Fault diagnosis has been accomplished using discrete wavelet transform for denoising the signal. Mahalanobis distance criteria has been employed for selecting the strongest feature on the extracted relevant features. Finally, these selected features have been passed to the SVM classifier for identifying and classifying the various bearing defects. The results reveal that the vibration signatures obtained from developed non-contact sensor compare well with the accelerometer data obtained under the same conditions. A developed sensor is a promising tool for detecting the bearing damage and identifying its class. SVM results have established the effectiveness of the developed non-contact sensor as a vibration measuring instrument which makes the developed sensor a cost-effective tool for the condition monitoring of rotating machines.

Extreme learning machines (ELMs) basically give answers to two fundamental learning problems: (1) Can fundamentals of learning (i.e., feature learning, clustering, regression and classification) be made without tuning hidden neurons (including biological neurons) even when the output shapes and function modeling of these neurons are unknown? (2) Does there exist unified framework for feedforward neural networks and feature space methods? ELMs that have built some tangible links between machine learning techniques and biological learning mechanisms have recently attracted increasing attention of researchers in widespread research areas. This paper provides an insight into ELMs in three aspects, viz: random neurons, random features and kernels. This paper also shows that in theory ELMs (with the same kernels) tend to outperform support vector machine and its variants in both regression and classification applications with much easier implementation.

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models’ performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability.