Progress in Pattern RecognitionSameer Singh, Maneesha Singh Springer Science & Business Media, 3 ส.ค. 2007 - 243 หน้า Overview andGoals Pattern recognition has evolved as a mature field of data analysis and its practice involves decision making using a wide variety of machine learning tools. Over the last three decades, substantial advances have been made in the areas of classification, prediction, optimisation and planning algorithms. Inparticular, the advances made in the areas of non-linear classification, statistical pattern recognition, multi-objective optimisation, string matching and uncertainty management are notable. These advances have been triggered by the availability of cheap computing power which allows large quantities of data to be processed in a very short period of time, and therefore developed algorithms can be tested easily on real problems. The current focus of pattern recognition research and development is to take laboratory solutions to commercial applications. The main goal of this book is to provide researchers with some of the latest novel techniques in the area of pattern recognition, and to show the potential of such techniques on real problems. The book will provide an excellent background to pattern recognition students and researchers into latest algorithms for pattern matching, and classification and their practical applications for imaging and non-imaging applications. Organization and Features The book is organised in two parts. The first nine chapters of the book describe novel advances in the areas of graph matching, information fusion, data clustering and classification, feature extraction and decision making under uncertainty. |
เนื้อหา
Estimation in Feedback Loops by Stochastic Learning | 3 |
Combining Exhaustive and Approximate Methods for Improved SubGraph Matching | 17 |
Information Fusion Techniques for Reliably Training Intrusion Detection Systems | 27 |
Use of Artificial Neural Networks and Effects of Amino Acid Encodings in the Membrane Protein Prediction Problem | 37 |
Computationally Efficient Graph Matching via Energy Vector Extraction | 47 |
A Validity Index Based on Cluster Symmetry | 54 |
Introduction of New Expert and Old Expert Retirement in Ensemble Learning under Drifting Concept | 64 |
Comparison of Three Feature Extraction Techniques to Distinguish Between Different Infrasound Signals | 75 |
Online One Stroke Character Recognition Using Directional Features | 145 |
Comparison of SVMs in Number Plate Recognition | 152 |
Three Different Models for Named f Entity Recognition in Bengali | 161 |
Comparison of Local and Global Thresholding for Binarization of Cheque Images | 171 |
Reading out 2D Barcode PDF417 | 179 |
Offline HandWritten FarsiArabic Word Segmentation into Subword under Overlapped or Connected Conditions | 186 |
Iris Biometrics Algorithm for Low Cost Devices | 195 |
Optimization on PGA Based Face Recognition Models | 203 |
Developing Trading Strategies based on Riskanalysis of Stocks | 83 |
Facial Image Processing with Convolutional Neural Networks | 97 |
Timedependent Interactive Graphical Models for Human Activity Analysis | 109 |
A New Lexiconbased Method for Automated Detection of Terrorist Web Documents | 119 |
A Neural Network Approach for Multifont and SizeIndependent Recognition of Ethiopic Characters | 129 |
Automated Classification of Affective States using Facial Thermal Features | 138 |
Discriminating Unknown Faces using iJ Eigenface Approach and a Novelty Filter | 214 |
Using Timing to Detect Horror Shots in Horror Movies | 225 |
IndoorOutdoor Scene Classification using Audio and Video Features | 232 |
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คำและวลีที่พบบ่อย
accuracy algorithm amino acid applications approach audio barcode barcode symbol binary Biometrics centroids character classifier complex Computer Vision concept drift convolution corpus data set database decision tree duration Eigenfaces encodings ensemble estimator Euclidean distance evaluation experimental results expert retirement face image face recognition facial feature extraction feature maps feature vector filter graph matching Hidden Markov Model image processing Infrasound input Iris Kd-tree key phrases labeled layer linear machine learning Markov Model matrix maximum method motion intensity Named Entity Recognition neural network nodes normal novelty detection number of clusters number plate obtained optimal output packets paper parameters Pattern Recognition performance pixels prediction primitives probability probability vector problem proposed PS-index representative robust segmentation sequence shows signal step structure subspace subwords Support Vector Machines Sym-index Table tags techniques threshold tion training set transform validation wavelet words