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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Rec…
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Guitar Pattern Recognition System – The NEW Way To Visualize Your Fretboard
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Ensemble Methods: Foundations and Algorithms (Chapman & Hall/CRC Data Mining and Knowledge Discovery Serie)
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Problem Solving As Pattern Recognition: The Method of Proton-Seconds
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Kernel Methods for Pattern Analysis
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Matrix Methods in Data Mining and Pattern Recognition, Second Edition
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Pattern Recognition: Concepts, Methods and Applications
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Optimization for Computer Vision: An Introduction to Core Concepts and Methods (Advances in Computer Vision
and Pattern Re…
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Energy Minimization Methods in Computer Vision and Pattern Recognition: 7th International Conference, EMMCVPR 2009, Bonn, …
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Pattern Recognition: Methods and Applications
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Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling
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Pattern Recognition: Applications and Methods: 4th International Conference, ICPRAM 2015, Lisbon, Portugal, January 10-12,…
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Springer Proceedings in Mathematics \u0026 Statistics: Mathematical Methodologies in Pattern Recognition and Machine Learning: Contributions from the International Conference on Pattern Recognition Applica
On order equivalences between distance and similarity measures on sequences and trees.- Scalable Corpus Annotation by Graph Construction and Label Propagation.- Computing the reeb graph for triangle meshes with active contours.- Efficient Computation of Voronoi Neighbors based on Polytope search in Pattern Recognition.- Estimation of the common oscillation for Phase Locked Matrix Factorization.- ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines.- Pitch-sensitive Components emerge from Hierarchical Sparse Coding of Natural Sounds.- Generative Embeddings based on Rican Mixtures: Application to KernelBased Discriminative Classification of Magnetic Resonance Images.-Single-Frame Signal Recovery Using a Similarity-Prior Based on Pearson Type VII MRF.- Tracking solutions of time varying linear inverse problems.- Stacked Conditional Random Fields Exploiting Structural Consistencies.- Segmentation of Vessel Geometries from Medical Images using GPF Deformable Model.- Robust Deformable Model for Segmenting the Left Ventricle in 3D volumes of Ultrasound Data.- Algorithm to maintain linear element in 3D Level Set Topology Optimization.- Facial Expression recognition using Log-Euclidean statistical shape models. Springer Proceedings in Mathematics \u0026 Statistics: Mathematical Methodologies in Pattern Recognition and Machine Learning: Contributions from the International Conference on Pattern Recognition Applica
Statistical Methods for Pattern Recognition (Paperback)
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 with 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)
Pattern Recognition and Machine Learning (Hardcover)
This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning me
thods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries. • Author: Y Anzai • ISBN:9780120588305 • Format:Hardcover • Publication Date:1992-07-14
Cism International Centre for Mechanical Sciences: Algebraic Methods in Pattern Recognition: Course Held at the Department of Automation and Information, July 1971 (Paperback)
Algebraic Methods in Pattern Recognition: Course Held at the Department of Automation and Information, July 1971 • Author: Juliusz Kulikowski • ISBN:9783211811283 • Format:Paperback • Publication Date:1973-06-26
Stroke Analysis on CT Images : A Pattern Recognition Approach (Paperback)
Stroke Analysis on CT Images: A Pattern Recognition Approach discusses briefly everything from stroke, it’s causes, conventional methods of stroke analysis on CT images to analysis using texture properties and classification of CT images. Texture analysis and other pattern recognition approaches, for stroke analysis, are largely discussed in this book with examples and great number of formulas, and real results. A great reference for beginners Stroke Analysis on CT Images: A Pattern Recognition Approach (Paperback)
Pattern Recognition on Oriented Matroids (Hardcover)
This book covers a range of innovative problems in combinatorics, poset and graph theories, optimization, and number theory that constitute a far-reaching extension of committee methods in pattern recognition. The universal language of oriented matr Pattern Recognition on Oriented Matroids covers a range of innovative problems in combinatorics, poset and graph theories, optimization, and number theory that constitute a far-reaching extension of the arsenal of committee methods in pattern recognition. The groundwork for the modern committee theory was laid in the mid-1960s, when it was shown that the familiar notion of solution to a feasible system of linear inequalities has ingenious analogues which can serve as collective solutions to infeasible systems. A hierarchy of dialects in the language of mathematics, for instance, open cones in the context of linear inequality systems, regions of hyperplane arrangements, and maximal covectors (or topes) of oriented matroids, provides an excellent opportunity to take a fresh look at the infeasible system of homogeneous strict linear inequalities – the standard working model for the contradictory two-class pattern recognition problem in its geometric setting. The universal language of oriented matroid theory considerably simplifies a structural and enumerative analysis of applied aspects of the infeasibility phenomenon. The present book is devoted to several selected topics in the emerging theory of pattern recognition on oriented matroids: the questions of existence and applicability of matroidal generalizations of committee decision rules and related graph-theoretic constructions to oriented matroids with very weak restrictions on their structural properties; a study (in which, in particular, interesting subsequences of the Farey sequence appear naturally) of the hierarchy of the corresponding tope committees; a description of the three-tope committees that are the most attractive approximation to the notion of solution to an infeasible system of linear constraints; an application of convexity in oriented matroids as well as blocker constructions in combinatorial optimization and in poset theory to enumerative problems on tope committees; an attempt to clarify how elementary changes (one-element reorientations) in an oriented matroid affect the family of its tope committees; a discrete Fourier analysis of the important family of critical tope committees through rank and distance relations in the tope poset and the tope graph; the characterization of a key combinatorial role played by the symmetric cycles in hypercube graphs. Contents Oriented Matroids, the Pattern Recognition Problem, and Tope Committees Boolean Intervals Dehn-Sommerville Type Relations Farey Subsequences Blocking Sets of Set Families, and Absolute Blocking Constructions in Posets Committees of Set Families, and Relative Blocking Constructions in Posets Layers of Tope Committees Three-Tope Committees Halfspaces, Convex Sets, and Tope Committees Tope Committees and Reorientations of Oriented Matroids Topes and Critical Committees Critical Committees and Distance Signals Symmetric Cycles in the Hypercube Graphs Pattern Recognition on Oriented Matroids covers a range of innovative problems in combinatorics, poset and graph theories, optimization, and number theory that constitute a far-reaching extension of the arsenal of committee methods in pattern recognition. The groundwork for the modern committee theory was laid in the mid-1960s, when it was shown that the familiar notion of solution to a feasible system of linear inequalities has ingenious analogues which can serve as collective solutions to infeasible systems. A hierarchy of dialects in the language of mathematics, for instance, open cones in the context of linear inequality systems, regions of hyperplane arrangements, and maximal covectors (or topes) of oriented matroids, provides an excellent opportunity to take a fresh look at the infeasible system of homogeneous strict linear inequalities – the standard working model for the contradictory two-class pattern recognition problem in its geometric setting. The universal language of oriented matroid theory considerably simplifies a structural and enumerative analysis of applied aspects of the infeasibility phenomenon. The present book is devoted to several selected topics in the emerging theory of pattern recognition on oriented matroids: the questions of existence and applicability of matroidal generalizations of committee decision rules and related graph-theoretic constructions to oriented matroids with very weak restrictions on their structural properties; a study (in which, in particular, interesting subsequences of the Farey sequence appear naturally) of the hierarchy of the corresponding tope committees; a description of the three-tope committees that are the most attractive approximation to the notion of solution to an infeasible system of linear constraints; an application of convexity in oriented matroids as well as blocker constructions in combinatorial optimization and in poset theory to enumerative problems on tope committees; an attempt to clarify how elementary changes (one-element reorientations) in an oriented matroid affect the family of its tope committees; a discrete Fourier analysis of the important family of critical tope committees through rank and distance relations in the tope poset and the tope graph; the characterization of a key combinatorial role played by the symmetric cycles in hypercube graphs. Contents Oriented Matroids, the Pattern Recognition Problem, and Tope Committees Boolean Intervals Dehn-Sommerville Type Relations Farey Subsequences Blocking Sets of Set Families, and Absolute Blocking Constructions in Posets Committees of Set Families, and Relative Blocking Constructions in Posets Layers of Tope Committees Three-Tope Committees Halfspaces, Convex Sets, and Tope Committees Tope Committees and Reorientations of Oriented Matroids Topes and Critical Committees Critical Committees and Distance Signals Sym
metric Cycles in the Hypercube Graphs
Pattern Recognition and Neural Networks (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)
