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Data Analysis, Learning Symbolic & Numeric Knowledge

Data Analysis, Learning Symbolic & Numeric Knowledge Proceedings Of The Conference On Data Analysis, Learning Symbolic & Numeric Knowledge;

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Clustering Methodology for Symbolic Data

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical;

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Symbolic Data Analysis and the SODAS Software

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical;

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Symbolic Data Analysis

data have their own internal structure, which must be taken into account in any analysis. This text presents a unified account of symbolic data;

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty

This book constitutes the refereed proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with;

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Mathematica for Theoretical Physics

Mathematica (R) to derive numeric and symbolic solutions. Delivers dozens of fully interactive examples for learning and implementation, constants;

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Numeric Data Services and Sources for the General Reference Librarian

The proliferation of online access to social science statistical and numeric data sources, such as the U.S. Census Bureau's American Fact;

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Mathematica for Theoretical Physics

Mathematica (R) to derive numeric and symbolic solutions. Delivers dozens of fully interactive examples for learning and implementation, constants;

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Statistical Learning and Data Science

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream;

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Statistical Learning and Data Science

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream;

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Computer Algebra 2006

in symbolic summation, factorization, symbolic-numeric linear algebra and linear functional equations. The papers were presented at a workshop;

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Advances in Data Science

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are;

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Multilingual Subject

analysis of this data, the book looks at the relationship between symbolic form and the development of a multilingual subjectivity; links with;

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Learning pandas -

, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and;

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Knowledge Science, Engineering and Management

Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge;

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Knowledge Science, Engineering and Management

Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge;

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Discovery Science

knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various;

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Machine Learning for Knowledge Discovery with R

of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling;

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Discovery Science

includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data;

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Operational Risk Management

unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis. Key Features: * The book is presented in four;

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Maple V

between numeric computation and symbolic computation, illustrating how both are used in Maple V Release 5. Extensive how-to examples are;

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Computational Analysis of Structured Media

of deterministic and random composites. Schwarz's method and functional equations yield for use in symbolic-numeric computations relevant to the effective;

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Symbolic Mathematics for Chemists

An essential guide to using Maxima, a popular open source symbolic mathematics engine to solve problems, build models, analyze data and;

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Advances in Knowledge Discovery and Data Mining

supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and;

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Advances in Knowledge Discovery and Data Mining

supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and;

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Advances in Knowledge Discovery and Data Mining

supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and;

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Sentiment Analysis and Knowledge Discovery in Contemporary Business

source that discusses applications of sentiment analysis as well as data mining, machine learning algorithms, and big data streams in business;

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