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Data-Variant Kernel Analysis

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book;

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Kernel Methods for Remote Sensing Data Analysis

foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis;

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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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Multivariate Kernel Smoothing and Its Applications

Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st;

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Multivariate Kernel Smoothing and Its Applications

Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st;

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Kernel Smoothing

Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets.The basic principle is that local;

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Kernels For Structured Data

applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several;

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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

Although the application of reproducing kernel has been explored in different fields in the past twenty to thirty years and the relevant;

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Bridging The Gap Between Graph Edit Distance And Kernel Machines

In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition;

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Linux Kernel Development

of Linux Kernel Development includes new and updated material throughout the book: An all-new chapter on kernel data structures Details on;

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Kernel Smoothing

of kernel smoothing in a variety of contexts, considering independent and correlated data e.g. with short-memory and long-memory correlations, as;

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The Statistical Analysis of Doubly Truncated Data - With Applications in R

A thorough treatment of the statistical methods used to analyze doubly truncated data In The Statistical Analysis of Doubly Truncated Data;

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Asymmetric Kernel Smoothing

kernel functions. Further, it discusses the statistical properties of estimators and test statistics using asymmetric kernels. The topics;

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Kernel Ridge Regression in Clinical Research

kernel trick for reduced arithmetic complexity, estimation of uncertainty by Gaussians unlike histograms, corrected data-overfit by ridge;

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Clustering

This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to;

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A Guide to Kernel Exploitation

in the Linux kernel. Finally, Part IV wraps up the analysis on kernel exploitation and looks at what the future may hold.;

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Data Warehouse Designs

a key competitive advantage. Time Variant data enables data warehouses to directly associate events in the past with the participants;

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Data Warehouse Designs

a key competitive advantage. Time Variant data enables data warehouses to directly associate events in the past with the participants;

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Data Science for Wind Energy

analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods;

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Data Science for Wind Energy

analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods;

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New Research in Financial Markets

research interests of European scholars in financial economics. The first section uses empirical analysis of financial market data to test the;

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Variant Construction from Theoretical Foundation to Applications

This open access book presents theoretical framework and sample applications of variant construction. The first part includes the;

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Outlier Analysis

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods;

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Outlier Analysis

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods;

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Sequence Classification Using Support Vector Machines

statistical analysis of the data set and of the results. The results of the different methods will be compared and discussed in detail. The last;

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