From the streets of London to subway stations in New York City, hundreds of thousands of surveillance cameras ubiquitously collect hundreds of thousands of videos, often running 24/7. How can such vast volumes of video data be stored, analyzed, indexed, and searched? How can advanced video analysis and systems autonomously recognize people and detect targeted activities real-time? Collating and presenting the latest information Intelligent Video Surveillance: Systems and Technology explores these issues, from fundamentals principle to algorithmic design and system...
From the streets of London to subway stations in New York City, hundreds of thousands of surveillance cameras ubiquitously collect hundreds of thou...
Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread application in machine learning, neural networks, pattern recognition, image processing, and computer vision. Filling a void in the literature, Manifold Learning Theory and Applications incorporates state-of-the-art techniques in manifold learning...
Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningfu...
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed "ensemble learning" by researchers in computational intelligence and machine learning, it is known to improve a decision system's robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as "boosting" and "random forest" facilitate solutions to key computational issues such as face recognition...
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic socie...
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning
Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings,...
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called i...
This book presents advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph and graph in vector spaces, and describes their real-world applications.
This book presents advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 gra...
Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.
Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and appli...
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed "ensemble learning" by researchers in computational intelligence and machine learning, it is known to improve a decision system's robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as "boosting" and "random forest" facilitate solutions to key computational issues such as face recognition...
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic socie...
Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group...
Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image proces...
Offering a detailed, unified approach, this book examines advances and applications of Support Vector Machines: image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, artificial intelligence and more.
Offering a detailed, unified approach, this book examines advances and applications of Support Vector Machines: image processing, medical practice, co...
