Notice bibliographique
- Notice
Type(s) de contenu et mode(s) de consultation : Texte noté : électronique
Auteur(s) : Witten, Ian H.
Titre(s) : Data mining [Texte électronique] : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall
Édition : 3rd ed.
Publication : Burlington, MA : Morgan Kaufmann, cop. 2011
Description matérielle : 1 ressource dématérialisée
Collection : [Morgan Kaufmann series in data management systems]
Note(s) : Includes bibliographical references (pages 587-605) and index
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding
in machine learning concepts as well as practical advice on applying machine learning
tools and techniques in real-world data mining situations. This highly anticipated
third edition of the most acclaimed work on data mining and machine learning will
teach you everything you need to know about preparing inputs, interpreting outputs,
evaluating results, and the algorithmic methods at the heart of successful data mining.
Thorough updates reflect the technical changes and modernizations that have taken
place in the field since the last edition, including new material on Data Transformations,
Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version
of the popular Weka machine learning software developed by the authors. Witten, Frank,
and Hall include both tried-and-true techniques of today as well as methods at the
leading edge of contemporary research
Autre(s) auteur(s) : Frank, Eibe. Fonction indéterminée
Hall, Mark Andrew. Fonction indéterminée
Sujet(s) : Exploration de données
Bases de données -- Interrogation
Indice(s) Dewey :
006.312 (23e éd.) = Exploration des données (informatique) ; 005.7 (23e éd.) = Données informatiques ; 519.53 (23e éd.) = Statistiques descriptives, analyse multivariée, analyse de la variance et de la
covariance
Identifiants, prix et caractéristiques : ISBN 9780123748560
Identifiant de la notice : ark:/12148/cb44634190q
Notice n° :
FRBNF44634190
(notice reprise d'un réservoir extérieur)
Table des matières : Part I ; Machine Learning Tools and Techniques:1 ; What's iIt all about?;2 ; Input:
concepts, instances, and attributes;3 ; Output: knowledge representation;4 ; Algorithms:
the basic methods;5 ; Credibility: evaluating what's been learned --Part II ; Advanced
Data Mining: 6. ; Implementations: real machine learning schemes;7 ; Data transformation;8
; Ensemble learning;9 ; Moving on: applications and beyond --Part III ; The Weka Data
MiningWorkbench: 10. ; Introduction to Weka;11 ; The explorer --12 ; The knowledge
flow interface;13 ; The experimenter;14 ; The command-line interface;15 ; Embedded
machine learning;16 ; Writing new learning schemes;17 ; Tutorial exercises for the
weka explorer.
