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Kernel Methods for Pattern Analysis

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data;

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Data-Variant Kernel Analysis

appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data;

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Kernel Methods for Remote Sensing Data Analysis

Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have;

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Kernel Learning Algorithms for Face Recognition

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced;

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Kernel Mode Decomposition and the Programming of Kernels

alternative (programming) approach to the kernel selection problem while using mode decomposition as a prototypical pattern recognition problem;

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Bridging The Gap Between Graph Edit Distance And Kernel Machines

combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a;

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Digital Signal Processing with Kernel Methods

algorithms for classification and detection problems in the context of signal processing * Surveys advances in kernel signal processing beyond SVM;

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Spatial Epidemiological Approaches in Disease Mapping and Analysis

, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.;

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Spatial Epidemiological Approaches in Disease Mapping and Analysis

, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.;

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Nonlinear Numerical Analysis in Reproducing Kernel Space

in the constructing theory research of the reproducing kernel space since 1980's, and worked out a series of specific structural methods for;

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Kernel Ridge Regression in Clinical Research

entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition;

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Regularization, Optimization, Kernels, and Support Vector Machines

-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine;

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Regularization, Optimization, Kernels, and Support Vector Machines

, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing;

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Kernels For Structured Data

kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.;

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Kernel Mean Embedding of Distributions

A Hilbert space embedding of a distribution-in short, a kernel mean embedding-has recently emerged as a powerful tool for machine learning;

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Combinatorics of Set Partitions

combinatorics of pattern avoidance and pattern enumeration in set partitions. Designed for students and researchers in discrete mathematics, the book;

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Handbook of Spatial Point-Pattern Analysis in Ecology

gap, Handbook of Spatial Point-Pattern Analysis in Ecology shows how the techniques of point-pattern analysis are useful for tackling;

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Structural, Syntactic, and Statistical Pattern Recognition

; spatio-temporal pattern recognition; structural matching; text and document analysis.;

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Kernel Smoothing

data examples Kernel Smoothing: Principles, Methods and Applications is a textbook for senior undergraduate and graduate students in statistics;

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Introduction to Data Mining for the Life Sciences

. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as;

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Computer Analysis of Images and Patterns

multimedia, model-based vision approaches, motion analysis, natural computation for digital imagery, segmentation and grouping, and shape;

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Computer Analysis of Images and Patterns

multimedia, model-based vision approaches, motion analysis, natural computation for digital imagery, segmentation and grouping, and shape;

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep;

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Statistical and Machine Learning Approaches for Network Analysis

weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross;

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Machine Learning and Knowledge Discovery in Databases

; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical;

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Machine Learning and Knowledge Discovery in Databases

; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical;

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Machine Learning and Knowledge Discovery in Databases

; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and;

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