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