Singh Maneesha Singh Sameer (51 results)

Paperback. Condition: Very Good. Sorry, No international shipping on this item.Ex-library paperback in very nice condition with the usual markings and attachments.

Hardcover. Condition: Good to Very Good. Dust Jacket Condition: Published Without Dust Jacket. 1st Edition. 242 pp. Tightly bound. Lower corners are lightly bumped. Boards show light shelf rubbing. Text is free of markings. No ownership markings. Published without dust jacket. First Edition / First Printing. 9,8,7,6,5,4,3,2,1.…

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Condition: New. pp. 830 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Condition: New. pp. 830.

Condition: New. pp. 830.

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Condition: New. pp. xiii + 243 2007th edition.

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Condition: New. pp. xiii + 243.

Condition: New. pp. xiii + 243.

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Condition: New. In English.

Condition: New. In English.

Condition: New. Pattern Recognition and Data Mining.- Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies.- Mathematical Features for Recognizing Preference in Sub-saharan African Traditional Rhythm Timelines.- Empirical Bounds on Error .

Taschenbuch. Condition: Neu. Pattern Recognition and Data Mining | Third International Conference on Advances in Pattern Recognition, ICAR 2005, Bath, UK, August 22-25, 2005, Part I | Sameer Singh (u. a.) | Taschenbuch | xxvi | Englisch | 2005 | Springer | EAN 9783540287575 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Pattern Recognition and Data Mining.- Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies.- Mathematical Features for Recognizing Preference in Sub-saharan African Traditional Rhythm Timelines.- Empirical Bounds on Error Differences When Using Naive Bayes.- Effective Probability Forecasting for Time Series Data Using Standard Machine Learning Techniques.- A Continuous Weighted Low-Rank Approximation for Collaborative Filtering Problems.- GP Ensemble for Distributed Intrusion Detection Systems.- Clustered Trie Structures for Approximate Search in Hierarchical Objects Collections.- On Adaptive Confidences for Critic-Driven Classifier Combining.- The RW2 Algorithm for Exact Graph Matching.- Making Use of Unelaborated Advice to Improve Reinforcement Learning: A Mobile Robotics Approach.- Consolidated Trees: Classifiers with Stable Explanation. A Model to Achieve the Desired Stability in Explanation.- Discovering Predictive Variables When Evolving Cognitive Models.- Mathematical Morphology and Binary Geodesy for Robot Navigation Planning.- Neural Network Classification: Maximizing Zero-Error Density.- Taxonomy of Classifiers Based on Dissimilarity Features.- Combination of Boosted Classifiers Using Bounded Weights.- Prediction of Commodity Prices in Rapidly Changing Environments.- Develop Multi-hierarchy Classification Model: Rough Set Based Feature Decomposition Method.- On Fitting Finite Dirichlet Mixture Using ECM and MML.- Disease Classification from Capillary Electrophoresis: Mass Spectrometry.- Analyzing Large Image Databases with the Evolving Tree.- A Sequence Labeling Method Using Syntactical and Textual Patterns for Record Linkage.- Recognition Tasks Are Imitation Games.- Use of Input Deformations with Brownian Motion Filters forDiscontinuous Regression.- Hierarchical Clustering of Dynamical Systems Based on Eigenvalue Constraints.- An Optimally Weighted Fuzzy k-NN Algorithm.- A Tabu Search Based Method for Minimum Sum of Squares Clustering.- Approximation of Digital Circles by Regular Polygons.- A Novel Feature Fusion Method Based on Partial Least Squares Regression.- Combining Text and Link Analysis for Focused Crawling.- A Weighting Initialization Strategy for Weighted Support Vector Machines.- Configuration of Neural Networks for the Analysis of Seasonal Time Series.- Boosting Feature Selection.- Similarity Searching in Image Retrieval with Statistical Distance Measures and Supervised Learning.- Using Patterns to Generate Prime Numbers.- Empirical Study on Weighted Voting Multiple Classifiers.- Spectral Clustering for Time Series.- A New EM Algorithm for Resource Allocation Network.- A Biased Support Vector Machine Approach to Web Filtering.- A New Approach to Generate Frequent Patterns from Enterprise Databases.- Consolidated Tree Classifier Learning in a Car Insurance Fraud Detection Domain with Class Imbalance.- Missing Data Estimation Using Polynomial Kernels.- Predictive Model for Protein Function Using Modular Neural Approach.- Using kNN Model for Automatic Feature Selection.- Multi-view EM Algorithm for Finite Mixture Models.- Segmentation Evaluation Using a Support Vector Machine.- Detection of Spots in 2-D Electrophoresis Gels by Symmetry Features.- Analysis of MHC-Peptide Binding Using Amino Acid Property-Based Decision Rules.- Accuracy of String Kernels for Protein Sequence Classification.- An Efficient Feature Selection Method for Object Detection.- Multi-SOMs: A New Approach to Self Organised Classification.- Selection of Classifiers Using Information-Theoretic Criteria.- ICA and GA Feature Extraction and Selection for Cloud Classification.- Signal Processing.- A Study on Robustness of Large Vocabulary Mandarin Chinese Continuous Speech Recognition System Based on Wavelet Analysis.- Recognition of Insect Emissions Applying the Discrete Wavelet Transform.- On the Performance of Hurst-Vectors for Speaker Identification S.…

Condition: New. pp. 724.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - International Workshop on Pattern Recognition for Crime Prevention, Security and Surveillance.- Biometrics.- Image Processing.- Medical Imaging.

Paperback. Condition: Brand New. 1st edition. 809 pages. 9.00x6.00x1.25 inches. In Stock.

Condition: New.

Condition: New. In English.

Taschenbuch. Condition: Neu. Progress in Pattern Recognition | Sameer Singh (u. a.) | Taschenbuch | Advances in Computer Vision and Pattern Recognition | xiii | Englisch | 2010 | Springer | EAN 9781849966832 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…