Statistical Pattern Recognition A Review

Springer Theses: Electro-Optic Photonic Circuits : From Linear and Nonlinear Waves in Nanodisordered Photorefractive Ferroelectrics (Hardcover)

Graph-based Representations in Pattern Recognition : 9th Iapr-Tc-15 International Workshop, Gbrpr 2013, Vienna, Austria, May 15-17, 2013, Proceedings (Paperback)

Lecture Notes in Computer Science: Structural, Syntactic, and Statistical Pattern Recognition: Joint Iapr International Workshop, S+sspr 2018, Beijing, China, August 17-19, 2018, Proceedings, Series No. 11004 (2018 Edition) (Paperback)

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This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S]SSPR 2018, held in Beijing, China, in August 2018. The 49 papers presented in this volume were carefully reviewed and selected from 75 submissions. They were organized in topical sections named: classification and clustering; deep learning and neurla networks; dissimilarity representations and Gaussian processes; semi and fully supervised learning methods; spatio-temporal pattern recognition and shape analysis; structural matching; multimedia analysis and understanding; and graph-theoretic methods. • ISBN:9783319977843 • Format:Paperback • Publication Date:2018-08-02

Graph Kernels.- A Graph Kernel from the Depth-Based Representation.- Incorporating Molecule’s Stereoisomerism within the Machine Learning Framework.- Transitive State Alignment for the Quantum Jensen-Shannon Kernel.- Clustering.- Balanced K-Means for Clustering.- Poisoning Complete-Linkage Hierarchical Clustering.- A Comparison of Categorical Attribute Data Clustering Methods.- Graph Edit Distance.- Improving Approximate Graph Edit Distance Using Genetic Algorithms.- Approximate Graph Edit Distance Guided by Bipartite Matching of Bags of Walks.- A Hausdorff Heuristic for Efficient Computation of Graph Edit Distance.- Graph Models and Embedding.- Flip-Flop Sublinear Models for Graphs.- Node Centrality for Continuous-Time Quantum Walks.- Max-Correlation Embedding Computation.- Discriminant Analysis.- Fast Gradient Computation for Learning with Tensor Product Kernels and Sparse Training Labels.- Nonlinear Discriminant Analysis Based on Probability Estimation by Gaussian Mixture Model.- Combining and Selecting.- Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood.- Majority Vote of Diverse Classifiers for Late Fusion.- Entropic Graph Embedding via Multivariate Degree Distributions.- On Parallel Lines in Noisy Forms.- Metrics and Dissimilarities.- Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance.- Matching Similarity for Keyword-Based Clustering.- Applications.- Quantum vs Classical Ranking in Segment Grouping.- Remove Noise in Video with 3D Topological Maps.- Video Analysis of a Snooker Footage Based on a Kinematic Model.- Partial Supervision.- Evaluating Classification Performance with only Positive and Unlabeled Samples.- Who Is Missing? A New Pattern Recognition Puzzle.- Poster Session.- Edit Distance Computed by Fast Bipartite Graph Matching.- Statistical Method for Semantic Segmentation of Dominant Plane from Remote Exploration Image Sequence.- Analyses on Generalization Error of Ensemble Kernel Regressors.- Structural Human Shape Analysis for Modeling and Recognition.- On Cross-Validation for MLP Model Evaluation.- Weighted Mean Assignment of a Pair of Correspondences Using Optimisation Functions.- Chemical Symbol Feature Set for Handwritten Chemical Symbol Recognition.- About Combining Metric Learning and Prototype Generation.- Tracking System with Re-identification Using a RGB String Kernel.- Towards Scalable Prototype Selection by Genetic Algorithms with Fast Criteria.- IOWA Operators and Its Application to Image Retrieval.- On Optimum Thresholding of Multivariate Change Detectors.- Commute Time for a Gaussian Wave Packet on a Graph.- Properties of Object-Level Cross-Validation Schemes for Symmetric Pair-Input Data.- A Binary Factor Graph Model for Biclustering.- Improved BLSTM Neural Networks for Recognition of On-Line Bangla Complex Words.- A Ranking Part Model for Object Detection.- Regular Decomposition of Multivariate Time Series and Other Matrices.- Texture Synthesis: From Convolutional RBMs to Efficient Deterministic Algorithms.- Improved Object Matching Using Structural Relations.- Designing LDPC Codes for ECOC Classification Systems.- Unifying Probabilistic Linear Discriminant Analysis Variants in Biometric Authentication. • ISBN:9783662444146 • Format:Paperback • Publication Date:2014-08-04

This volume contains all papers presented at SSPR 2004 and SPR 2004, hosted by the Instituto de Telecomunicac~, oes/Instituto Superior T\\u0026acute; ecnico, Lisbon, Portugal, August 18-20, 2004. This was the fourth time that the two workshops were held back-to-back. The SSPR was the tenth International Workshop on Structural and Synt- tic Pattern Recognition, and the SPR was the ?fth International Workshop on Statistical Techniques in Pattern Recognition. These workshops have traditi- ally been held in conjunction with ICPR (International Conference on Pattern Recognition), and are the major events for technical committees TC2 and TC1, respectively, of the International Association for Pattern Recognition (IAPR). The workshops were closely coordinated, being held in parallel, with plenary talks and a common session on hybrid systems. This was an attempt to resolve thedilemmaofhowto dealwiththeneedfornarrow-focusspecializedworkshops yet accommodate the presentation of new theories and techniques that blur the distinction between the statistical and the structural approaches. A total of 219 papers were received from many countries, with the subm- sion and reviewing processes being carried out separately for each workshop. A total of 59 papers were accepted for oral presentation and 64 for posters. In – dition, four invited speakers presented informative talks and overviews of their research. They were: Alberto Sanfeliu, from the Technical University of Cata- nia, Spain; Marco Gori, from the University of Siena, Italy; Nello Cristianini, from the University of California, USA; and Erkki Oja, from Helsinki University of Technology, Finland, winner of the 2004 Pierre Devijver Award. • ISBN:9783540225706 • Format:Paperback • Publication Date:2004-07-28

The purpose of this book is to present some statistical methods of pattern recognition. The book brings contributions in the field of statistical pattern recognition both from a point of theoretical view and from a point of applications view which are achieved in a private field of pattern recognition: iris recognition. The book contains five chapters and four annexes.The algorithms from my book show the joining of some known results wit
h some observations or remarks which lead to the achievement of some very general algorithms, which are suitable for solving some complex pattern problems. I shall achieve the software implementation for the project methods using the programming language Matlab 7.0. A lot of the original contributions from this book constitute the object of some meaningful papers, which are international recognized through their publication in some impressive specialty journals or books from the international and national publishing houses. My significant scientific results were published in over 20 articles appeared in prestigious national or international journals. At least 8 of my papers are well reviewed in dedicated journals. Statistical Methods for Pattern Recognition (Paperback)

This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback. Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them. Pattern Recognition and Neural Networks (Paperback)