Structural, Syntactic, and Statistical Pattern Recognition: Joint Iapr International Workshops, S+sspr 2020, Padua, Italy, January 21-22, 2021, Procee, Torsello Andrea, Rossi Luca, Pelillo Marcello
Автор: Dit-Yan Yeung; James T. Kwok; Ana Fred; Fabio Roli Название: Structural, Syntactic, and Statistical Pattern Recognition ISBN: 3540372369 ISBN-13(EAN): 9783540372363 Издательство: Springer Рейтинг: Цена: 20263.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the proceedings of the 11th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2006 and the 6th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2006, held in Hong Kong, August 2006 alongside the Conference on Pattern Recognition, ICPR 2006.
Автор: Antonio Robles-Kelly; Marco Loog; Battista Biggio; Название: Structural, Syntactic, and Statistical Pattern Recognition ISBN: 3319490540 ISBN-13(EAN): 9783319490540 Издательство: Springer Рейтинг: Цена: 10342.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition, S+SSPR 2016, consisting of the International Workshop on Structural and Syntactic Pattern Recognition SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR.
Автор: Pasi Fr?nti; Gavin Brown; Marco Loog; Francisco Es Название: Structural, Syntactic, and Statistical Pattern Recognition ISBN: 3662444143 ISBN-13(EAN): 9783662444146 Издательство: Springer Рейтинг: Цена: 8944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Gabriel Ferrate; Theo Pavlidis; Alberto Sanfeliu; Название: Syntactic and Structural Pattern Recognition ISBN: 3642834647 ISBN-13(EAN): 9783642834646 Издательство: Springer Рейтинг: Цена: 11173.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Proceedings of the NATO Advanced Research Workshop on Syntactic and Structural Pattern Recognition, held in Barcelona-Sitges, Spain, October 23-25, 1986
Автор: Terry Caelli; Adnan Amin; Robert P.W. Duin; Mohame Название: Structural, Syntactic, and Statistical Pattern Recognition ISBN: 3540440119 ISBN-13(EAN): 9783540440116 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the proceedings of the 9th International Workshop on Structural and Syntactic Pattern Recognition and the 4th International Workshop on Statistical Techniques in Pattern Recognition held in Canada in 2002.
Автор: Niels da Vitoria Lobo; Takis Kasparis; Michael Geo Название: Structural, Syntactic, and Statistical Pattern Recognition ISBN: 3540896880 ISBN-13(EAN): 9783540896883 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Contains papers organized in topical sections on graph-based methods, probabilistic and stochastic structural models for PR, image and video analysis, shape analysis, kernel methods, recognition and classification, applications, ensemble methods, feature selection, density estimation and clustering, and computer vision and biometrics.
Автор: Edwin R. Hancock; Richard C Wilson; Terry Windeatt Название: Structural, Syntactic, and Statistical Pattern Recognition ISBN: 3642149790 ISBN-13(EAN): 9783642149795 Издательство: Springer Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume in the Springer Lecture Notes in Computer Science (LNCS) series contains the papers presented at the S+SSPR 2010 Workshops, which was the seventh occasion that SPR and SSPR workshops have been held jointly. S+SSPR 2010 was organized by TC1 and TC2, Technical Committees of the International Association for Pattern Recognition(IAPR), andheld inCesme, Izmir, whichis a seaside resort on the Aegean coast of Turkey. The conference took place during August 18-20, 2010, only a few days before the 20th International Conference on Pattern Recognition (ICPR) which was held in Istanbul. The aim of the series of workshops is to create an international forum for the presentation of the latest results and exchange of ideas between researchers in the ?elds of statistical and structural pattern recognition. SPR 2010 and SSPR 2010 received a total of 99 paper submissions from many di?erent countries around the world, giving it a truly international perspective, as has been the case for previous S+SSPR workshops. This volume contains 70 accepted papers, 39 for oral and 31 for poster presentation. In addition to par- lel oral sessions for SPR and SSPR, there were two joint oral sessions of interest to both SPR and SSPR communities. Furthermore, to enhance the workshop experience, there were two joint panel sessions on "Structural Learning" and "Clustering," in which short author presentations were followed by discussion. Another innovation this year was the ?lming of the proceedings by Videol- tures.
Автор: K.S. Fu; J.E. Albus; R.H. Anderson; J.M. Brayer; R Название: Syntactic Pattern Recognition, Applications ISBN: 3642664407 ISBN-13(EAN): 9783642664403 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The many different mathematical techniques used to solve pattem recognition problems may be grouped into two general approaches: the decision-theoretic (or discriminant) approach and the syntactic (or structural) approach.
Описание: This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020.
Описание: 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.
Автор: Ana Fred; Terry Caelli; Robert P.W. Duin; Aur?lio Название: Structural, Syntactic, and Statistical Pattern Recognition ISBN: 3540225706 ISBN-13(EAN): 9783540225706 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume contains all papers presented at SSPR 2004 and SPR 2004, hosted by the Instituto de Telecomunicac , oes/Instituto Superior T 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.
Автор: Flasinski Название: Syntactic Pattern Recognition ISBN: 9813278463 ISBN-13(EAN): 9789813278462 Издательство: World Scientific Publishing Рейтинг: Цена: 21384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This unique compendium presents the major methods of recognition and learning used in syntactic pattern recognition from the 1960s till 2018. Each method is introduced firstly in a formal way. Then, it is explained with the help of examples and its algorithms are described in a pseudocode. The survey of the applications contains more than 1,000 sources published since the 1960s. The open problems in the field, the challenges and the determinants of the future development of syntactic pattern recognition are discussed.This must-have volume provides a good read and serves as an excellent source of reference materials for researchers, academics, and postgraduate students in the fields of pattern recognition, machine perception, computer vision and artificial intelligence.
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