Showing results for "Pattern Recognition and Machine Learning English"

"Pattern Recognition: Exploring the Power of Data Analysis and Prediction through Cutting-Edge Technology" is a comprehensive guide to the field of pattern recognition, written by a team of experts in the field.The book covers the fundamentals of data analysis and statistical inference before delving into the theory and application of pattern recognition techniques. The authors explore a variety of methods, including statistical pattern recognition, machine learning, and deep learn...

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated computational libraries to under...

This book explains the basic principles of pattern recognition (PR) and machine learning (ML) in an easy-to-understand manner for beginners who are trying to learn these principles on their own. Readers with a basic knowledge of linear algebra and probability theory will find it easy to follow.Many excellent books in this field have been published in the past. However, these books are not necessarily intended for self-study by beginners.This book limits the topics to the mi...

This book constitutes the refereed proceedings of the 9th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2021, held in December 2021. Due to COVID-19 pandemic the conference was held virtually.The 62 full papers included in this book were carefully reviewed and selected from 215 submissions. They were organized in topical sections as follows: image processing; text, voice and video processing; bioinformatics; deep learning; medical image processing;...

This book highlights recent research on computer recognition systems, one of the most promising directions in artificial intelligence. Offering the comprehensive study on this field to date, it gathers 11 carefully selected articles contributed by experts on pattern recognition.The accepted papers address current computer science and computer systems related technological challenges and solutions, as well as many practical applications areas of pattern recognition and machine learni...

The LNCS volume constitutes the refereed proceedings of 10th International Conference, PReMI 2023, in Kolkata, India, in December 2023.The 91 full papers, presented together with abstracts of 6 keynote and invited talks, were carefully reviewed and selected from more than 300 submissions. The conference presents topics covering different aspects of pattern recognition and machine intelligence with real life state-of-the-art applications.

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially ...

This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018.The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods fo...

Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling outlines key knowledge in this area, combining critical introductory approaches with the latest advanced techniques. Beginning with an introduction of univariate and multivariate statistical analysis, the book then explores multivariate calibration and validation methods. Soft modeling in chemical data analysis, hyperspectral data analysis, and autoencoder applications in analytical che...

This book constitutes the proceedings of the 7th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2017,held in Kolkata, India, in December 2017.The total of 86 full papers presented in this volume were carefully reviewed and selected from 293 submissions. They were organized in topical sections named: pattern recognition and machine learning; signal and image processing; computer vision and video processing; soft and natural computing; speech and natu...

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and ho...