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Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods

This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and...

Advances in Data Analysis: Proceedings of the 30th Annual Conference
Advances in Data Analysis: Proceedings of the 30th Annual Conference
The book focuses on exploratory data analysis, learning of latent structures in datasets, and unscrambling of knowledge. It covers a broad range of methods from multivariate statistics, clustering and classification, visualization and scaling as well as from data and time series analysis. It provides new approaches for information retrieval and...
Data Science: Innovative Developments in Data Analysis and Clustering (Studies in Classification, Data Analysis, and Knowledge Organization)
Data Science: Innovative Developments in Data Analysis and Clustering (Studies in Classification, Data Analysis, and Knowledge Organization)

This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection...

Meta-Learning in Computational Intelligence (Studies in Computational Intelligence)
Meta-Learning in Computational Intelligence (Studies in Computational Intelligence)

In the early days of pattern recognition and statistical data analysis life was rather simple: datasets were relatively small, collected from well-designed experiments, analyzed using a few methods that had good theoretical background. Explosive growth of the use of computers led to the creation of huge amounts of data of all kinds,...

Robust Statistics: Theory and Methods (Probability and Statistics)
Robust Statistics: Theory and Methods (Probability and Statistics)
Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and...
Practical Text Mining with Perl (Wiley Series on Methods and Applications in Data Mining)
Practical Text Mining with Perl (Wiley Series on Methods and Applications in Data Mining)
Provides readers with the methods, algorithms, and means to perform text mining tasks

This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet (www.perl.org). It covers mining ideas from several perspectives—statistics, data mining,...

Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics)
Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics)

Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and...

Post Quantum Cryptography
Post Quantum Cryptography
Quantum computers will break today's most popular public-key cryptographic systems, including RSA, DSA, and ECDSA. This book introduces the reader to the next generation of cryptographic algorithms, the systems that resist quantum-computer attacks: in particular, post-quantum public-key encryption systems and post-quantum public-key signature...
Intelligent Data Analysis
Intelligent Data Analysis
This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The ten coherently written chapters by leading experts provide complete coverage of the core issues.

The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of...

Multivariate Public Key Cryptosystems (Advances in Information Security)
Multivariate Public Key Cryptosystems (Advances in Information Security)

Multivariate public key cryptosystems (MPKC) is a fast-developing new area in cryptography. In the past 10 years, MPKC schemes have increasingly been seen as a possible alternative to number theoretic-based cryptosystems such as RSA, as they are generally more efficient in terms of computational effort. As quantum computers are developed, MPKC...

Algorithms for Computer Algebra
Algorithms for Computer Algebra

Algorithms for Computer Algebra is the first comprehensive textbook to be published on the topic of computational symbolic mathematics. The book first develops the foundational material from modern algebra that is required for subsequent topics. It then presents a thorough development of modern computational algorithms for such...

Introduction to the Mathematical and Statistical Foundations of Econometrics (Themes in Modern Econometrics)
Introduction to the Mathematical and Statistical Foundations of Econometrics (Themes in Modern Econometrics)

This book is intended for use in a rigorous introductory PhD level course in econometrics, or in a field course in econometric theory. It covers the measure-theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit...

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