Download Supervised And Unsupervised Pattern Recognition

Supervised And Unsupervised Pattern Recognition

DOWNLOAD
Author by : Evangelia Miche Tzanakou
Languange Used : en
Release Date : 2017-12-19
Publisher by : CRC Press

ISBN : 9781420049770

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume....

Supervised And Unsupervised Pattern Recognition

DOWNLOAD
Author by : Evangelia Miche Tzanakou
Languange Used : en
Release Date : 1999-12-28
Publisher by : CRC Press

ISBN : 0849322782

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume....

Sampling Techniques For Supervised Or Unsupervised Tasks

DOWNLOAD
Author by : Frédéric Ros
Languange Used : en
Release Date : 2019-10-26
Publisher by : Springer Nature

ISBN : 9783030293499

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the “curse of dimensionality”, their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task. Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks; Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality; Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. "This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge." M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas "In science the difficulty is not to have ideas, but it is to make them work" From Carlo Rovelli...

Supervised And Unsupervised Machine Learning For Pattern Recognition And Time Series Prediction

DOWNLOAD
Author by : Kathryn Brenda Bean
Languange Used : en
Release Date : 2008
Publisher by :

ISBN : OCLC:297220545

I have developed a new framework to improve the performance of a partition-typed algorithm for the clustering of datasets with missing attributes. Chapter 3 describes this framework, and this approach is based on (Bean K., 2008). I have incorporated CLARA, PAM and K-means within a framework that remains general enough to allow other clustering algorithms to be used. Initial clustering is performed using a very fast algorithm: BIRCH. This approach was implemented to determine input parameters for a more accurate algorithm and to make the prediction of missing attributes more efficiently....

Pattern Recognition And Classification

DOWNLOAD
Author by : Geoff Dougherty
Languange Used : en
Release Date : 2012-10-28
Publisher by : Springer Science & Business Media

ISBN : 9781461453239

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning....

Supervised And Unsupervised Learning For Data Science

DOWNLOAD
Author by : Michael W. Berry
Languange Used : en
Release Date : 2019-09-04
Publisher by : Springer Nature

ISBN : 9783030224752

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning....

Supervised And Unsupervised Pattern Recognition

DOWNLOAD
Author by : Evangelia Miche Tzanakou
Languange Used : en
Release Date : 2017-12-19
Publisher by : CRC Press

ISBN : 1420049771

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume....

Artificial Neural Networks In Pattern Recognition

DOWNLOAD
Author by : Friedhelm Schwenker
Languange Used : en
Release Date : 2010-03-25
Publisher by : Springer Science & Business Media

ISBN : 9783642121586

This book constitutes the refereed proceedings of the 4th IAPR TC3 Workshop, ANNPR 2010, held in Cairo, Eqypt, in April 2010. The 23 revised full papers presented were carefully reviewed and selected from 42 submissions. The major topics of ANNPR are supervised and unsupervised learning, feature selection, pattern recognition in signal and image processing, and applications in data mining or bioinformatics....

Progress In Pattern Recognition Image Analysis Computer Vision And Applications

DOWNLOAD
Author by : José Ruiz-Shulcloper
Languange Used : en
Release Date : 2013-11-04
Publisher by : Springer

ISBN : 9783642418228

The two-volume set LNCS 8258 and 8259 constitutes the refereed proceedings of the 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013, held in Havana, Cuba, in November 2013. The 137 papers presented, together with two keynotes, were carefully reviewed and selected from 262 submissions. The papers are organized in topical sections on mathematical theory of PR, supervised and unsupervised classification, feature or instance selection for classification, image analysis and retrieval, signals analysis and processing, applications of pattern recognition, biometrics, video analysis, and data mining....

Artificial Intelligence Algorithms For Unsupervised Learning Clustering And Pattern Recognition With Neural Networks Examples With Matlab

DOWNLOAD
Author by : CESAR PERZ LOPEZ
Languange Used : en
Release Date :
Publisher by : SCIENTIFIC BOOKS

ISBN :

Artificial Intelligence combines mathematical algorithms and techniques from Machine Learning, Deep Learning and Big Data to extract the knowledge contained in the data and present it in an understandable and automatic way. Neural networks and their applications are a fundamental tool to develop work in Artificial Intelligence. On the other hand, unsupervised learning is more closely aligned with Artificial Intelligence as it gives the idea that a machine can learn to identify complex processes and patterns without the need for a human to provide guidance and supervision throughout the learning process. Some examples of unsupervised learning algorithms include clustering and association rules. In the case of this type of learning, there is no pre-training data set; the problem is approached blindly and only with logical operations to guide it. Although at first glance it seems impossible, it is about the ability to solve complex problems using only input data and logical algorithms. This avoids the use of reference data. Unsupervised learning algorithms are used to discover hidden patterns in unlabeled data. Unlike supervised learning algorithms, where there is prior knowledge of the desired answers, these algorithms do not have a set of ordered data. They are responsible for determining the most important common characteristics of a group of information and then grouping them according to their similarities. Among the most interesting models are the neural networks. MATLAB implementrs the Deep Learning Toolbox specialized in the techniques of analytics based on neural networks. Throughout this book the techniques of analytics for clustering and classification based on neural networks are developed using MATLAB software...

Welcome to Read Book Online