Bücher der Reihe Chapman & Hall/CRC Computer Science & Data Analysis

Provides a broad treatment of the theory and linguistic applications of semisupervised methods. This book discusses natural language processing methods, such as self-training and co-training. It focuses on machine learning techniques, including the boundary-oriented methods of perceptrons, boosting, SVMs, and the null-category noise model.

Offering key information on how to work with research data, this title presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education.

Due to its data handling and modeling capabilities as well as its flexibility, R is the most widely used software in bioinformatics. This title explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems. It covers the general properties of the R language.

This book includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students.

Combinatorial Inference in Geometric Data Analysis gives an overview of multidimensional statistical inference methods applicable to clouds of points that make no assumption on the process of generating data or distributions, and that are not based on random modelling but on permutation procedures recasting in a combinatorial fr

This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a p