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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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Graph Kernels
well as a description of desirable properties and requirements for benchmark data sets. Finally, the authors outline the future trends and open;
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Bridging The Gap Between Graph Edit Distance And Kernel Machines
of patterns in the graph domain - commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit;
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Kernel Methods and Estimation of Extreme Value Index
probability density functions of ultra-high frequency financial data. In Part II we study in detail the domain of attraction (DOA) approach for the;
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Theory of Reproducing Kernels and Applications
results.Chapter 5 deals with ordinary differential equations; Chapter 6 includes many concrete results for various fundamental partial differential;
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Regularization, Optimization, Kernels, and Support Vector Machines
is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.;
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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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Structured Search for Big Data
Model for Database and Information Systems and holds four US Patents in Structured Search and Data Integration. Conceptualizes;
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Generalized Mercer Kernels and Reproducing Kernel Banach Spaces
, separability, implicit representation, imbedding, compactness, representer theorem for learning methods, oracle inequality, and universal approximation;
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Deep Learning with Structured Data
identifying cats in YouTube videos. Powerful deep learning techniques can efficiently extract insight from the kind of structured data collected by;
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Asymmetric Kernel Smoothing
estimation with weakly dependent data. Further, the large- and finite-sample properties of estimators and test statistics smoothed by asymmetric;
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Big Data in Complex Systems
in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format;
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Heat Kernels And Spectral Theory
An advanced monograph on a central topic in the theory of differential equations, Heat Kernels and S;...
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Learning with Support Vector Machines
from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with;
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Cambridge Tracts in Mathematics
for the convergence of the expansions in infinite series. References are given to work on numerical methods of solution. Individual chapters;
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Machine Learning Pocket Reference Working with Structured Data in Python
learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value;
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Weighted Norm Inequalities for Integral Transforms with Product Kernals
functions and at the same time, to present solutions of two-weighted problems for them which are much more complicated than one-weighted ones;
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A Structured Programming Approach to Data
A Structured Programming Approach to Data is een boek van Derek Coleman;
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Semantic Keyword-based Search on Structured Data Sources
Conference on semantic Keyword-based Search on Structured Data Sources, IKC 2015, held in Coimbra, Portugal, in September 2015. The 13 revised;
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Isotropic and Anisotropic Seismic Tomography Using Active Source and Earthquake Records
the ground-truth data obtained from the TAiwan Integrated GEodynamics Research (TAIGER) active-source experiments that provides excellent and;
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Parallel Algorithms in Computational Science and Engineering
This contributed volume highlights two areas of fundamental interest in high-performance computing: core algorithms for important kernels;
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Sharing of Probabilistically Correlated Data in Peer-To-Peer Networks
entry. The partitioning of the distributed index of standard structured overlays is optimized towards single key lookups and joint data access;
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Beginning Apache Spark 2
you how to work with it. Along the way, you'll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn;
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Principles of Database Query Processing for Advanced Applications
query processing and optimization techniques for structured and unstructured data, written with the non-expert in mind, with a minimum;
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Kernel Mode Decomposition and the Programming of Kernels
. In this approach, kernels are programmed for the task at hand through the programming of interpretable regression networks in the context;
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