Search results for: supervised-and-unsupervised-pattern-recognition

Supervised and Unsupervised Pattern Recognition

Author : Evangelia Miche Tzanakou
File Size : 87.60 MB
Format : PDF, Docs
Download : 447
Read : 650
Download »
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.

The Industrial Electronics Handbook

Author : J. David Irwin
File Size : 57.48 MB
Format : PDF, Kindle
Download : 799
Read : 1289
Download »
From traditional topics that form the core of industrial electronics, to new and emerging concepts and technologies, The Industrial Electronics Handbook, in a single volume, has the field covered. Nowhere else will you find so much information on so many major topics in the field. For facts you need every day, and for discussions on topics you have only dreamed of, The Industrial Electronics Handbook is an ideal reference.

Handbook of Chemometrics and Qualimetrics

Author :
File Size : 72.67 MB
Format : PDF, ePub, Docs
Download : 969
Read : 513
Download »

Supervised and Unsupervised Pattern Recognition

Author : Evangelia Miche Tzanakou
File Size : 70.79 MB
Format : PDF, Kindle
Download : 852
Read : 789
Download »
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

Author : Frédéric Ros
File Size : 67.54 MB
Format : PDF, Kindle
Download : 327
Read : 271
Download »
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

Pattern Recognition and Image Preprocessing

Author : Sing T. Bow
File Size : 88.74 MB
Format : PDF, ePub, Mobi
Download : 480
Read : 440
Download »
Describing non-parametric and parametric theoretic classification and the training of discriminant functions, this second edition includes new and expanded sections on neural networks, Fisher's discriminant, wavelet transform, and the method of principal components. It contains discussions on dimensionality reduction and feature selection; novel computer system architectures; proven algorithms for solutions to common roadblocks in data processing; computing models including the Hamming net, the Kohonen self-organizing map, and the Hopfield net; detailed appendices with data sets illustrating key concepts in the text; and more.

Similarity Based Pattern Recognition

Author : Edwin Hancock
File Size : 55.63 MB
Format : PDF, Docs
Download : 772
Read : 544
Download »
This book constitutes the proceedings of the Second International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2013, which was held in York, UK, in July 2013. The 18 papers presented were carefully reviewed and selected from 33 submissions. They cover a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, from theoretical issues to real-world practical applications, and offer a timely picture of the state of the art in the field.

Pattern Recognition

Author : Sankar K. Pal
File Size : 20.97 MB
Format : PDF, ePub, Docs
Download : 336
Read : 892
Download »
This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource. Contents: Pattern Recognition: Evolution of Methodologies and Data Mining (A Pal & S K Pal); Adaptive Stochastic Algorithms for Pattern Classification (M A L Thathachar & P S Sastry); Shape in Images (K V Mardia); Decision Trees for Classification: A Review and Some New Results (R Kothari & M Dong); Syntactic Pattern Recognition (A K Majumder & A K Ray); Fuzzy Sets as a Logic Canvas for Pattern Recognition (W Pedrycz & N Pizzi); Neural Network Based Pattern Recognition (V David Sanchez A); Networks of Spiking Neurons in Data Mining (K Cios & D M Sala); Genetic Algorithms, Pattern Classification and Neural Networks Design (S Bandyopadhyay et al.); Rough Sets in Pattern Recognition (A Skowron & R Swiniarski); Automated Generation of Qualitative Representations of Complex Objects by Hybrid Soft-Computing Methods (E H Ruspini & I S Zwir); Writing Speed and Writing Sequence Invariant On-line Handwriting Recognition (S-H Cha & S N Srihari); Tongue Diagnosis Based on Biometric Pattern Recognition Technology (K Wang et al.); and other papers. Readership: Graduate students, researchers and academics in pattern recognition.

Pattern Recognition in Practice II

Author : L.N. Kanal
File Size : 30.42 MB
Format : PDF
Download : 573
Read : 1256
Download »
The 1985 Amsterdam conference brought together researchers active in pattern recognition methodology and the development of practical applications. The first part of the book covers various methodological aspects of image processing, knowledge based and model driven image understanding systems, 3-D reconstruction methods, and application oriented papers. Part II deals with aspects of statistical pattern recognition, the problem of population classification, and topics common to both pattern recognition and artificial intelligence.

In Situ Monitoring of Fiber Reinforced Composites

Author : Markus G.R. Sause
File Size : 42.12 MB
Format : PDF, ePub
Download : 469
Read : 412
Download »
“...a comprehensive and well written book, which...will be useful reading for both researchers entering the field and experienced specialists looking for new ideas....a valuable and long-lasting contribution to experimental mechanics.” – Stepan Lomov, KU Leuven This expert volume, an enhanced Habilitation thesis by the head of the Materials Testing Research Group at the University of Augsburg, provides detailed coverage of a range of inspection methods for insitu characterization of fiber-reinforced composites. The failure behavior of fiber reinforced composites is a complex evolution of microscopic damage phenomena. Beyond the use of classical testing methods, the ability to monitor the progression of damage insitu offers new ways to interpret the materials failure modes. Methods covered include digital image correlation, acoustic emission, electromagnetic emission, computed tomography, thermography, shearography, and promising method combinations. For each method, the discussion includes operational principles and practical applications for quality control as well as thoughtful assessment of the method's strengths and weakness so that the reader is equipped to decide which method or methods are most appropriate in a given situation. The book includes extensive appendices covering common experimental parameters influencing comparability of acoustic emission measurements; materials properties for modeling; and an overview of terms and abbreviations.