Identificacion Principal del registro INGC-EBK-000196

Control Number Identifier AR-LpUFI

LAST MODIFICATION DATE 20160826111930

CONTROL FIELD cr nn 008mamaa

CONTROL FIELD 130911s2014 gw | s |||| 0|eng d

a International Standard Book Number 9783319009605

a Standard number or code 10.1007/978-3-319-00960-5

a Personal name Grabczewski, Krzysztof.

a Title Meta-Learning in Decision Tree Induction

h Medium [libro electrónico] /

c Statement of responsibility, etc by Krzysztof Grabczewski.

a Place of publication, distribution, etc Cham :

b Name of publisher, distributor, etc Springer International Publishing :

b Name of publisher, distributor, etc Imprint: Springer,

c Date of publication, distribution, etc 2014.

a Extent xvi, 343 p. :

a Series statement Studies in Computational Intelligence,

x International Standard Serial Number 1860-949X ;

v Volume number/sequential designation 498

a Formatted contents note Introduction -- Techniques of decision tree induction -- Multivariate decision trees -- Unified view of decision tree induction algorithms -- Intemiâ_"advanced meta-learning framework -- Meta-level analysis of decision tree induction.

a Summary, etc The book focuses on different variants of decision tree induction but also describes  the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.  .

a Topical term or geographic name as entry element Engineering.

a Topical term or geographic name as entry element Artificial intelligence.

a Topical term or geographic name as entry element Computational intelligence.

a Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).

u Uniform Resource Identifier (R) http://dx.doi.org/10.1007/978-3-319-00960-5

c item type EBK

a descripción COM