Bart de moor (33 resultados)

Condición: Very Good.

Condición: Very Good.

Hardcover. Condición: Very Good. With dust jacket. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

hardcover. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Condición: New.

Paperback. Condición: New. Exact and Approximate Modeling of Linear Systems elegantly introduces the behavioural approach to mathematical modeling, an approach that requires models to be viewed as sets of possible outcomes rather than to be a priori bound to particular representations. The authors discuss exact and approximate fitting of data by linear, bilinear, and quadratic static models and linear dynamic models, a formulation that enables readers to select the most suitable representation for a particular purpose.This book presents exact subspace-type and approximate optimization-based identification methods, as well as representation-free problem formulations, an overview of solution approaches, and software implementation. Readers will find an exposition of a wide variety of modeling problems starting from observed data. The presented theory leads to algorithms that are implemented in C language and in MATLAB.…

hardcover. Condición: Good. No dust jacket. Ex-library book with typical stickers and stampings. Priority Mail is available on this item. No international shipping.

Condición: Very Good.

Condición: As New. Unread book in perfect condition.

hardcover. Condición: Very Good. Yellowed dust jacket. Priority Mail is available on this item. No international shipping.

Paperback. Condición: Brand New. new title edition. 206 pages. 9.75x7.00x0.50 inches. In Stock.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: New. In English.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: Fine. Dutch In de Eerste Wereldoorlog vluchtte een kwart van de Belgische bevolking voor het oorlogsgeweld. Tot vandaag leven verhalen van deze vluchtelingen voort in vele families die toen getroffen werden. Ze vertonen gelijkenissen met verhalen van asielzoekers die vandaag in ons land aankloppen. Ook zij zijn op de vlucht: voor oorlog, persoonlijke vervolging, uitbuiting, of gewoon voor de wanhoop die zich van hun leven meester maakt. Altijd een verkeerde tijd, op een verkeerde plaats. Net als toen. ?Verkeerde tijd, verkeerde plaats? is een wisselende reeks van ontmoetingen met vluchtelingen in België vandaag. Schrijver Bart Demyttenaere trok een tijdlang op met hedendaagse vluchtelingen. Bij het schrijven van zijn boek stuitte hij ook op de onvermoede verhalen van zijn beide oma's, vluchtelingen van 1914-18. Dit boek is geïllustreerd met foto's van twee fotografen. Nick Hannes toont de etappes van de asielprocedure in België. Dieter Telemans maakte portretten van mensen die met succes of tevergeefs in ons land aanklopten en bezocht het illegale verblijf van afgewezenen.…

Hardback. Condición: New. This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.…

Paperback. Condición: New. Exact and Approximate Modeling of Linear Systems elegantly introduces the behavioural approach to mathematical modeling, an approach that requires models to be viewed as sets of possible outcomes rather than to be a priori bound to particular representations. The authors discuss exact and approximate fitting of data by linear, bilinear, and quadratic static models and linear dynamic models, a formulation that enables readers to select the most suitable representation for a particular purpose.This book presents exact subspace-type and approximate optimization-based identification methods, as well as representation-free problem formulations, an overview of solution approaches, and software implementation. Readers will find an exposition of a wide variety of modeling problems starting from observed data. The presented theory leads to algorithms that are implemented in C language and in MATLAB.…

Hardcover. Condición: Brand New. 308 pages. 9.25x6.25x1.00 inches. In Stock.

Condición: New.

Condición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

hardcover. Condición: New. In shrink wrap. Looks like an interesting title.

Condición: New.

Condición: Very Good.

Hardback. Condición: New. This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.…