Gruppo di Lettura su Ensemble di Macchine ad Apprendimento Automatico

PhD studies

A.A. 2004/05   -   HOME PAGE

Docenti:


http://www.disi.unige.it/person/MasulliF/didattica/rg-home04
Ultimo aggiornamento 19 Ottobre  2004


Obiettivi

Recently, driven by application needs, multiple classifier combinations have evolved into a practical and effective solution for real-world pattern recognition tasks. The idea appears in various disciplines (including Machine Learning, Neural Networks, Pattern Recognition, and Statistics) under several names: hybrid methods, combining decisions, multiple experts, mixture of experts, sensor fusion and many more. In some cases, the combination is motivated by the simple observation that classifier performance is not uniform across the input space and different classifiers excel in different regions. Under a Bayesian framework, integrating over expert distribution leads naturally to expert combination. The generalization capabilities of ensembles of learning machines have been interpreted in the framework of Statistical Learning Theory and in the related theory of Large Margin Classifiers. 


There are several ways to use more than one classifier in a classification problem. A first "averaging" approach consists of generating multiple hypotheses from a single or multiple learning algorithms, and combining them through majority voting or different linear and non linear combinations. A "feature-oriented" approach is based on different methods to build ensembles of learning machines by subdividing the input space (e.g., random subspace methods, multiple sensors fusion, feature transformation fusion). "Divide-and-conquer" approaches isolate the regions in input space on which each classifier performs well, and direct new input accordingly, or subdivide a complex learning problem in a set of simpler subproblems, recombining them using suitable decoding methods. A "sequential-resampling" approach builds multiple classifier systems using bootstrap methods in order to reduce variance (bagging) or jointly bias and unbiased variance (boosting). 

There are fundamental questions that need to be addressed for a practical use of this collection of approaches: What are the theoretical tools to interpret possibly in a unified framework this multiplicity of ensemble methods? What is gained and lost in a combination of experts, when is it preferable to alternative approaches? What types of data are best suitable to expert combination? What types of experts are best suited for combinations? What are optimal training methods for experts which are expected to participate in a collective decision? What combination strategies are best suited to a particular problem and to a particular distribution of the data? What are the statistical methods and the appropriate benchmark data to evaluate multiclassifier systems? 


Audience

Il corso e' rivolto agli studenti di dottorato e a tutti gli studenti della laurea specialistica interessati.Metodologia:

Il reading group si riunisce una volta alla settimana. A ogni riunione viene scelto a turno un partecinante  che illustrera' il testo  la riunione successiva e guidera' la discussione e sara' a disposizione durante la settimana per spiegazioni.

Bibliografia

Ludmila I. Kuncheva Combining Pattern Classifiers. Methods and Algorithms, Wiley, 2004.

Prerequisiti

Nessun corso in particolare, poiche' tutto e' presente nel testo di riferimento.
Il corso e' collegato a vari argomenti trattati in Soft Computing, Reti Neurali e Apprendimento Statistico.




Pagina ad accesso riservato (chiedere permesso di accesso al docente)

Orario (il corso si svolge nel primo semestre) mercoledi  ore 16.30 -18.00 stanza  208




Link interessanti