Автор: Shigeo Abe Название: Support Vector Machines for Pattern Classification ISBN: 1447125487 ISBN-13(EAN): 9781447125488 Издательство: Springer Рейтинг: Цена: 22201.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This guide on the use of SVMs in pattern classification includes a rigorous performance comparison of classifiers and regressors. The book takes the unique approach of focusing on classification rather than covering the theoretical aspects of SVMs.
Автор: Riesen Kaspar & Bunke Horst Название: Graph Classification And Clustering Based On Vector Space Embedding ISBN: 9814304719 ISBN-13(EAN): 9789814304719 Издательство: World Scientific Publishing Рейтинг: Цена: 17424.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Focuses on a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. This title aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.
Автор: Duda, Richard O. Название: Pattern classification with computer manual, 2r.e. ISBN: 0471703508 ISBN-13(EAN): 9780471703501 Издательство: Wiley Рейтинг: Цена: 27712.00 р. Наличие на складе: Поставка под заказ.
Описание: The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Автор: J?n Atli Benediktsson; Josef Kittler; Fabio Roli Название: Multiple Classifier Systems ISBN: 3642023258 ISBN-13(EAN): 9783642023255 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 8th International Workshop on Multiple Classifier Systems, MCS 2009, held in Reykjavik, Iceland, in June 2009. This work contains papers that are organized in topical sections on ECOC boosting and bagging, MCS in remote sensing, unbalanced data and decision templates, concept drift and SVM ensembles.
Автор: Xiang Xu; Xingkun Wu; Feng Lin Название: Cellular Image Classification ISBN: 3319476289 ISBN-13(EAN): 9783319476285 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis.
First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical tweezer applications, including selective cell pick-up, pairing, grouping or separation, as well as rotation of cell dimers and clusters. Both translational dragging force and rotational torque in the experiments are in good accordance with the theoretical model. With a simple all-fiber configuration, and low peak irradiation to targeted cells, instrumentation of this optical chuck technology will provide a powerful tool in the ANA-IIF laboratories. Chapters focus on the optical, mechanical and computing systems for the clinical trials. Computer programs for GUI and control of the optical tweezers are also discussed.
to more discriminative local distance vector by searching for local neighbors of the local feature in the class-specific manifolds. Encoding and pooling the local distance vectors leads to salient image representation. Combined with the traditional coding methods, this method achieves higher classification accuracy.
Then, a rotation invariant textural feature of Pairwise Local Ternary Patterns with Spatial Rotation Invariant (PLTP-SRI) is examined. It is invariant to image rotations, meanwhile it is robust to noise and weak illumination. By adding spatial pyramid structure, this method captures spatial layout information. While the proposed PLTP-SRI feature extracts local feature, the BoW framework builds a global image representation. It is reasonable to combine them together to achieve impressive classification performance, as the combined feature takes the advantages of the two kinds of features in different aspects.
Finally, the authors design a Co-occurrence Differential Texton (CoDT) feature to represent the local image patches of HEp-2 cells. The CoDT feature reduces the information loss by ignoring the quantization while it utilizes the spatial relations among the differential micro-texton feature. Thus it can increase the discriminative power. A generative model adaptively characterizes the CoDT feature space of the training data. Furthermore, exploiting a discriminant representation allows for HEp-2 cell images based on the adaptive partitioned feature space. Therefore, the resulting representation is adapted to the classification task. By cooperating with linear Support Vector Machine (SVM) classifier, this framework can exploit the advantages of both generative and discriminative approaches for cellular image classification.
The book is written for those researchers who would like to develop their own programs, and the working MatLab codes are included for all the important algorithms presented. It can also be used as a reference book for graduate students and senior undergraduates in the area of biomedical imaging, image feature extraction, pattern recognition an
Автор: Rokach Lior Название: Pattern Classification Using Ensemble Methods ISBN: 9814271063 ISBN-13(EAN): 9789814271066 Издательство: World Scientific Publishing Рейтинг: Цена: 13464.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.
Описание: This book covers hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Includes basic theory, use of type-2 fuzzy models, optimization of type-2 fuzzy systems and modular neural networks and more.
Описание: This thesis provides a systematic and integral answer to an open problem concerning the universality of dynamic fuzzy controllers. It presents a number of novel ideas and approaches to various issues including universal function approximation, universal fuzzy models, universal fuzzy stabilization controllers, and universal fuzzy integral sliding mode controllers. The proposed control design criteria can be conveniently verified using the MATLAB toolbox. Moreover, the thesis provides a new, easy-to-use form of fuzzy variable structure control. Emphasis is given to the point that, in the context of deterministic/stochastic systems in general, the authors are in fact discussing non-affine nonlinear systems using a class of generalized T-S fuzzy models, which offer considerable potential in a wide range of applications.
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