Pattern recognition

Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

Covers text mining and link detection algorithms, pre-processing techniques, knowledge representation, visualization and applications.

Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

A unique account of developing topic in data mining and machine learning.

This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

This book introduces different approaches to developing recommender systems that automate choice-making strategies to provide affordable, personal, and high-quality recommendations.

This book describes a range of string problems in computer science and molecular biology and the algorithms developed to solve them.

This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

A comprehensive and consistent theory of estimation, including a description of a powerful new tool, the generalized maximum capacity estimator.

This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

This book addresses research issues regarding multibiometrics for human identification, examining their advantages and evaluation.

Gives a solid basis for conducting performance evaluations of learning algorithms in practical settings with an emphasis on classification algorithms.

A systematic description of modeling face geometry from images, including information on mathematical tools, image processing and computer vision techniques.

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them.

Digital twin, blackchain, and wireless sensor networks can work together to improve services in the smart city.

This book contains papers presented at the 3rd International Conference on Cognitive- based Information Processing and Applications (CIPA) in Changzhou, China, from November 2--3, 2023.

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.

Stereoscopic processes are increasingly used in virtual reality and entertainment.

Stereoscopic processes are increasingly used in virtual reality and entertainment.

A new approach to the issue of data quality in pattern recognition Detailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining.

A new approach to the issue of data quality in pattern recognition Detailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining.

A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology.

A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology.

This book is the first of its kind to discuss error estimation with a model-based approach.

This book is the first of its kind to discuss error estimation with a model-based approach.

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004.

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004.

The first edition, published in 1973, has become a classic reference in the field.

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models.

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models.

An accessible undergraduate introduction to the concepts and methods in pattern recognition, machine learning and deep learning.

Now in its third edition, this book focuses on practical algorithms for mining data from even the largest datasets.

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

An intuitive and accessible textbook covering both fundamentals and applications.

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.

A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

This undergraduate textbook introduces essential machine learning concepts in NLP in a unified and gentle mathematical framework.

This undergraduate textbook introduces essential machine learning concepts in NLP in a unified and gentle mathematical framework.

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

A comprehensive survey of the quickly developing discipline of cognitive linguistics, its rich methodology, key results, and interdisciplinary context.