Xiaochun Wang - Böcker
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7 produkter
7 produkter
1 791 kr
Kommande
Data Compression for Data Mining Algorithms tackles the important problems in the design of more efficient data mining algorithms by way of data compression techniques and provides the first systematic and comprehensive description of the relationships between data compression mechanisms and the computations involved in data mining algorithms. Data mining algorithms are powerful analytical techniques used across various disciplines, including business, engineering, and science. However, in the big data era, tasks such as association rule mining and classification often require multiple scans of databases, while clustering and outlier detection methods typically depend on Euclidean distance for similarity measures, leading to high computational costs.Data Compression for Data Mining Algorithms addresses these challenges by focusing on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computations involved in tasks such as feature selection and similarity computation. The book features the latest developments in both lossless and lossy data compression methods and provides a comprehensive exposition of data compression methods for data mining algorithm design from multiple points of view.Key discussions include Huffman coding, scalar and vector quantization, transforms, subbands, wavelet-based compression for scalable algorithms, and the role of neural networks, particularly deep learning, in feature selection and dimensionality reduction. The book’s contents are well-balanced for both theoretical analysis and real-world applications, and the chapters are well organized to compose a solid overview of the data compression techniques for data mining. To provide the reader with a more complete understanding of the material, projects and problems solved with Python are interspersed throughout the text.Covers popular data compression methods and their solutions to aid in the development and application of data mining algorithmsIncludes projects and problems solved with Python to help readers create programs for both data compression and data mining problemsFocuses on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computationsSimplifies the content of the field of data compression by covering topics that are widely useful from a data mining perspective
Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
Inbunden, Engelska, 2019
1 472 kr
Skickas inom 10-15 vardagar
This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.
Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
Häftad, Engelska, 2020
1 064 kr
Skickas inom 10-15 vardagar
This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition.
New Developments in Unsupervised Outlier Detection
Algorithms and Applications
Inbunden, Engelska, 2020
1 840 kr
Skickas inom 10-15 vardagar
This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research.The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.
1 840 kr
Skickas inom 10-15 vardagar
This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research.The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.
2 498 kr
Skickas inom 10-15 vardagar
Anomaly detection in video surveillance stands at the core of numerous real-world applications that have broad impact and generate significant academic and industrial value. The key advantage of writing the book at this point in time is that the vast amount of work done by computer scientists over the last few decades has remained largely untouched by a formal book on the subject, although these techniques significantly advance existing methods of image and video analysis and understanding by taking advantage of anomaly detection in the data mining community and visual analysis in the computer vision community. The proposed book provides a comprehensive coverage of the advances in video based anomaly detection, including topics such as the theories of anomaly detection and machine perception for the functional analysis of abnormal events in general, the identification of abnormal behaviour and crowd abnormal behaviour in particular, the current understanding of computer vision development, and the application of this present understanding towards improving video-based anomaly detection in theory and coding with OpenCV. The book also provides a perspective on deep learning on human action recognition and behaviour analysis, laying the groundwork for future advances in these areas. Overall, the chapters of this book have been carefully organized with extensive bibliographic notes attached to each chapter. One of the goals is to provide the first systematic and comprehensive description of the range of data-driven solutions currently being developed up to date for such purposes. Another is to serve a dual purpose so that students and practitioners can use it as a textbook while researchers can use it as a reference book. A final goal is to provide a comprehensive exposition of the topic of anomaly detection in video media from multiple points of view.
2 651 kr
Kommande
In the big data era, many modern data mining problems cannot be solved efficiently by the traditional algorithms, (i.e., specifically intractable by a single-processor computing system, either spatially prohibitive, temporally prohibitive or both). Obtaining optimal solutions in a tolerable amount of time, parallel data mining algorithms can be chosen as a suitable tool to solve the aforesaid problems. However, most up-to-date books on parallel and distributed computing techniques, such as Hadoop and Spark, are not wholly on the data mining subjects, covering only a portion of the materials in one or a few chapters and touching on the subjects but without going into it deeply, and thus incomplete and lacking systematicity, specialty and comprehensiveness. Parallel Data Mining Algorithms uniquely combines systematicity and comprehensiveness, trying not only to present as many data mining algorithms developed throughout till now as possible but also to provide their parallel solutions developed by the research and industry community currently. Instead of scratching the surface, it covers a broad range of data mining algorithms in depth, provides a comprehensive coverage of the state of the arts and advances in parallel data mining algorithm research (covering such topics as the parallel algorithm design in general and the implementations by the divide-and-conquer scheme, the MapReduce programming model and the Resilient Distributed Dataset (RDD) in specific) and makes their design and analysis accessible to all levels of readers with self-contained chapters and algorithms in pseudocode and with updated notes and bibliography to reflect developments in the field, in the hope that it will become an introduction-level parallel data mining algorithm textbook in universities as well as the standard reference for professionals.