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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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Facial Expression Recognition

of human facial expressions and kernel principal component analysis (kernel PCA) with fractional polynomial power models. A mean vector of features;

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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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General Theory of Portfolio Efficiency

The mean-variance analysis produces optimal portfolio choices for individual investors taking asset prices and payoff distributions as;

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Kernel Methods for Remote Sensing Data Analysis

. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data;

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Distribution, Integral Transforms and Applications

theory from its beginnings to the point of proving many of the deep, important theorems, such as the Schwartz kernel theorem and the Malgrange;

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Distribution, Integral Transforms and Applications

theory from its beginnings to the point of proving many of the deep, important theorems, such as the Schwartz kernel theorem and the Malgrange;

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Statistical Tests Of Nonparametric Hypotheses

-Pearson and LeCam's theories of optimal tests, the theories of empirical processes and kernel estimators with extensions of their applications to;

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Mean Oscillations and Equimeasurable Rearrangements of Functions

-space version of the John-Nirenberg inequality for bounded mean oscillation functions with sharp exponent, and sharp embedding theorems for;

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Applied Statistical Science II

Processes & Applications; The Generalized Order Statistics; Some Inference Aspects of a Social Network; Estimation of Mean Based on an Unbalanced;

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Gaussian Random Processes

associated with mixing rate . The third problem involves estimation of an unknown mean value of a random process, this random process being;

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Business Statistics MCQs

, relative measure of skewness, coefficient of skewness, frequency distribution, relative frequency, frequency curve, arithmetic mean, average;

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Linux Kernel Development

Linux Kernel Development details the design and implementation of the Linux kernel, presenting the content in a manner that is beneficial;

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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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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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Asset Pricing in Discrete Time

Relying on the existence, in a complete market, of a pricing kernel, this book covers the pricing of assets, derivatives, and bonds in a;

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Advanced Mean Field Methods - Theory & Practice

This book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches;

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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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Linux Kernel Programming Part 2 - Char Device Drivers and Kernel Synchronization

understanding of working with hardware interrupts and kernel synchronization Key Features Delve into hardware interrupt handling, threaded IRQs;

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Szego Kernel Asymptotics for High Power of CR Line Bundles and Kodaira Embedding Theorems on CR Manifolds

Let $X$ be an abstract not necessarily compact orientable CR manifold of dimension $2n-1$, $ngeqslan;...

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Theory of Besov Spaces

covered within this volume. Additionally, related function spaces-Hardy spaces, bounded mean oscillation spaces, and Hoelder continuous spaces-are;

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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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Shrinkage Estimation

statistical properties. The book focuses primarily on point and loss estimation of the mean vector of multivariate normal and spherically symmetric;

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Probability Concepts in Engineering Planning and Design

Distributions 3.3.2. Covariance and Correlation 3.3.3. Conditional Mean and Variance 3.4. Concluding Remarks Problems 4. Functions of Random Variables;

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Linux Device Driver Development Cookbook

Over 30 recipes to develop custom drivers for your embedded Linux applications. Key Features Use Kernel facilities to develop powerful;

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Linux Kernel Programming

Brand-new edition of the comprehensive Linux kernel reference - 40% of material revised. Now updated throughout to cover Linux 2.4.;

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Opt Upper Limit

/rework cost and any product over the minimum incurs a "give-away" cost. The objective of the canning problem is to determine the target mean for;

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