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A First Course in Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
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Introduction to Pattern Recognition : Statistical, Structural, Neural and Fuzzy Logic Approaches (Series in Machine Percep…
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Pattern Recognition with Fuzzy Objective Function Algorithms (Advanced Applications in Pattern Recognition)
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Fuzzy C-means Clustering using Pattern Recognition: Concepts, Methods, Implementations
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Compression Schemes for Mining Large Datasets: A Machine Learning Perspective (Advances in Computer Vision and Pattern Rec…
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A First Course in Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
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Pattern Recognition Theory and Applications (NATO Asi Series: Series F: Computer & Systems Sciences)
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Fundamentals of Pattern Recognition and Machine Learni
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Ensemble Methods: Foundations and Algorithms (Chapman & Hall/CRC Data Mining and Knowledge Discovery Serie)
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Pattern Recognition
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Deep Learning through Sparse and Low-Rank Modeling (Computer Vision and Pattern Recognition)
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Lecture Notes in Networks and Systems: Progress in Image Processing, Pattern Recognition and Communication Systems : Proceedings of the Conference (Cores, Ip\u0026c, Acs) – June 28-30 2021 (Series #255) (Paperback)
Building an Ensemble of Classifiers via Randomized Models of Ensemble Members.- Hybrid learning model for syntactic pattern recognition.- Distance Metrics in Clustering and Weighted Scoring Algorithm.- Exploration of Hardware Acceleration Methods for an XNOR Traffic Signs Classifier.- ALEA: An Anonymous Leader Election Algorithm for Synchronous Distributed Systems.- Comparing concepts of quantum and classical neural network models for image classification task.- Can Color Cryptography Be Truly Random?.- Description-based Ranking of Visual Instances: Feasibility Study for Keypoints.- Fuzzy system for lip print identification.- Assessment of correlations between age and textural features of CT images of thoracic vertebrae.- Assessment of correlations between age and textural features of CT images of thoracic vertebrae.- Application of Image Entropy Analysis for the Quality Assessment of Stitched Images. Lecture Notes in Networks and Systems: Progress in Image Processing, Pattern Recognition and Communication Systems: Proceedings of the Conference (Cores, Ip\u0026c, Acs) – June 28-30 2021 (Paperback)
Machine Learning and Data Mining in Pattern Recognition : 7th International Conference, Mldm 2011, New York, Ny, Usa, August 30-September 3, 2011proceedings (Paperback)
This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2011, held in New York, NY, USA. The 44 revised full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining; and machine learning and image mining. • ISBN:9783642231988 • Format:Paperback • Publication Date:2011-08-12
Chapman \u0026 Hall/CRC Computer Science \u0026 Data Analysis: Pattern Recognition Algorithms for Data Mining (Paperback)
This valuable text addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. Organized into eight chapters, the book begins by introducing PR, data mining, and knowledge discovery concepts. The authors proceed to analyze the tasks of multi-scale data condensation and dimensionality reduction. Then
they explore the problem of learning with support vector machine (SVM), and conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm. Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm. • Author: Sankar K Pal,Pabitra Mitra • ISBN:9780367394240 • Format:Paperback • Publication Date:2019-09-21
Fuzzy Cluster Analysis With Application on Atherosclerosis Disease (Paperback)
Fuzzy cluster analysis is one of the advanced topics in statistics, which is a tool that assesses the relationships among samples of data set ( many attributes and many observations) by organizing the attributes into different clusters, each observations is belong to any cluster with probability between [0,1]. The fuzzy cluster analysis applied in many fields for example, medicine, cures of diseases or symptoms of diseases, psychiatry, data mining, image processes, pattern recognition, information retrieval. Fuzzy Cluster Analysis with Application on Atherosclerosis Disease (Paperback)
Optimized Thresholding on Self Organizing Map for Cluster Analysis (Paperback)
One of the popular tools in the exploratory phase of data mining and pattern recognition is the Kohonen Self Organizing Map (SOM). Recently, experiments have shown that to find the ambiguities involved in cluster analysis, it is not necessary to consider crisp boundaries in clustering operations. In this Book, the Incremental Leader algorithm for the thresholding of the SOM (Inc-SOM) is proposed to validate the potential of a crisp clustering algorithm. However, the performance deteriorates when there is overlap between clusters. To overcome the ambiguities in the results of cluster analysis, a rough thresholding for the SOM (Rough-SOM) is proposed. In Rough-SOM, the data is first trained by a SOM neural network, then the rough thresholding, which is a rough set based clustering approach, is applied on the neurons of the SOM. The optimal number of clusters can be found by rough set theory, which groups the neurons into a set of overlapping clusters. An optimization technique is applied during the last stage to assign the overlapped data to the true clusters. Optimized Thresholding on Self Organizing Map for Cluster Analysis (Paperback)
Clustering of Visual Information Using Spectral Methods (Paperback)
Data clustering emerged over the last \
