L'événement

Philippe Rodriguez

La Révolution Blockchain

Algorithmes ou institutions, à qui donnerez-vous votre confiance?

La Révolution Blockchain




 


email ou code client :
mot de passe :
* Oublié ?

Première visite ?
Demander un devis
Créer un compte
Frais de port
à 1 euros
pour les particuliers
à partir de 50 euros
(France métropolitaine uniquement)

Ajouter cet article au panier

Nbre d'exemplaires:

Encyclopedia of Machine Learning and Data Mining

Titre :

Encyclopedia of Machine Learning and Data Mining

Caractéristiques :


Auteur(s) :Geoffrey I. Webb et Claude Sammut
Editeur :SPRINGER VERLAG
Parution :03/2017
Langue :Anglais Anglais
ISBN :9781489976857
Reliure :Livre relié
Prix :634.00 € ttc
Disponibilité :Sortie prévue le 29/03/2017

Couverture :


Encyclopedia of Machine Learning and Data Mining

Résumé :

<div>This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.</div><div>Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.</div><div>The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.</div><div>
</div><div>
</div>

Table des matières :

Abduction.- Adaptive Resonance Theory.- Anomaly Detection.- Bayes Rule.- Case-Based Reasoning.- Categorical Data Clustering.- Causality.- Clustering from Data Streams.- Complexity in Adaptive Systems.- Complexity of Inductive Inference.- Computational Complexity of Learning.- Confusion Matrix.- Connections Between Inductive Inference and Machine Learning.- Covariance Matrix.- Decision List.- Decision Lists and Decision Trees.- Decision Tree.- Deep Learning.- Density-Based Clustering.- Dimensionality Reduction.- Document Classification.- Dynamic Memory Model.- Empirical Risk Minimization.- Error Rate.- Event Extraction from Media Texts.- Evolutionary Clustering.- Evolutionary Computation in Economics.- Evolutionary Computation in Finance.- Evolutionary Computational Techniques in Marketing.- Evolutionary Feature Selection and Construction.- Evolutionary Kernel Learning.- Evolutionary Robotics.- Expectation Maximization Clustering.- Expectation Propagation.- Feature Construction in Text Mining.- Feature Selection.- Feature Selection in Text Mining.- Gaussian Distribution.- Gaussian Process.- Generative and Discriminative Learning.- Grammatical Inference.- Graphical Models.- Hidden Markov Models.- Inductive Inference.- Inductive Logic Programming.- Inductive Programming.- Inductive Transfer.- Inverse Reinforcement Learning.- Kernel Methods.- K-Means Clustering.- K-Medoids Clustering.- K-Way Spectral Clustering.- Learning Algorithm Evaluation.- Learning Graphical Models.- Learning Models of Biological Sequences.- Learning to Rank.- Learning Using Privileged Information.- Linear Discriminant.- Linear Regression.- Locally Weighted Regression for Control.- Machine Learning and Game Playing.- Manhattan Distance.- Maximum Entropy Models for Natural Language Processing.- Mean Shift.- Metalearning.- Minimum Description Length Principle.- Minimum Message Length.- Mixture Model.- Model Evaluation.- Model Trees.- Multi Label Learning.- Naïve Bayes.- Occam's Razor.- Online Controlled Experiments and A/B Testing.- Online Learning.- Opinion Stream Mining .- PAC Learning.- Partitional Clustering.- Phase Transitions in Machine Learning.

Ajouter cet article au panier

Nbre d'exemplaires: