Deadline extention: July 1st, 2002 - School on ENSEMBLE METHODS FOR LEARNING MACHINES Vietri sul Mare 22-28 September 2002

[Francesco Masulli]

Dear All, 

Due to  many requests, arrived to the Directors of the Course,  to extend the 
deadline of  applications,  the deadline has been extended up to 
July 1st. There would not be other  extentions. 

Best regards 
Francesco Masulli 
================================================================

			  7th Course of the
   "International  School on Neural Nets Eduardo R. Caianiello" on

		ENSEMBLE METHODS FOR LEARNING MACHINES

    IIASS -Vietri sul Mare (Salerno)-ITALY          22-28 September 2002
	      web page:  http://www.iiass.it/school2002



JOINTLY ORGANIZED BY

IIASS-International Institute         EMFCSC-Ettore Majorana Foundation
for Advanced Scientifc Studies        and   
E.R. Caianiello,                      Center for Scientific Culture,
Vietri sul Mare (SA) Italy            Erice (TR) Italy


AIMS

In the  last decade,  ensemble methods have  shown to be  effective in
many  application  domains and  constitute  one  of  the main  current
directions  in Machine  Learning research.   This school  will address
from  a  theoretical  and  empirical  view  point,  several  important
questions  concerning   the  combination  of   Learning  Machines.  In
particular,  different  approaches  to  the problem  which  have  been
proposed  in the  context of  Machine Learning,  Neural  Networks, and
Statistical Pattern Recognition will be discussed. Moreover, a special
stress will  be given  to theoretical and  practical tools  to develop
ensemble  methods  and   evaluate  their  applications  on  real-world
domains, such as Remote Sensing, Bioinformatics and Medical field.



SPONSORS

GNCS-Gruppo Nazionale per il Calcolo Scientifico 
IEEE-Neural Networks Council 
INNS-International Neural Network Society 
SIREN-Italian Neural Networks Society 
University of Salerno,Italy 

 
DIRECTORS OF THE COURSE                   DIRECTORS OF THE SCHOOL

Nathan Intrator (Israel and USA)          Michael Jordan (USA)
Francesco  Masulli (Italy)                Maria Marinaro (Italy)
                   

		
LECTURERS

Leo Breiman, University of California at Berkeley, California, USA
Lorenzo Bruzzone, University of Trento, Trento, Italy
Thomas G. Dietterich, Oregon State University, Oregon, USA
Cesare Furlanello, Istituto per la Ricerca Scientifica e Tecnologica, Trento, 
Italy
Giuseppina C. Gini, Politecnico di Milano,  Milano, Italy
Tin Kam Ho, Bell Laboratories, New Jersey, USA
Nathan Intrator, Tel Aviv Univ., Israel and Brown Univ., Providence, Rhode 
Island, USA
Ludmila I. Kuncheva, University of Wales, Bangor, UK
Francesco  Masulli, University of Pisa, Italy 
Stefano Merler, Istituto per la Ricerca Scientifica e Tecnologica, Trento, 
Italy
Fabio Roli, University of Cagliari, Cagliari, Italy
Giorgio Valentini, University of Genova, Italy


PLACE
 
International Institute for Advanced Scientific Studies 
E.R. Caianiello (IIASS)
Via Pellegrino 19, 84019 Vietri sul Mare, Salerno  (Italy)


POETIC TOUCH

Vietri (from "Veteri", its ancient  Roman name) sul Mare ("on sea") is
located within  walking distance from Salerno and  marks the beginning
of the Amalfi  coast. Short rides take to  Positano, Sorrento, Pompei,
Herculaneum,  Paestum, Vesuvius,  or by  boat, the  islands  of Capri,
Ischia, and Procida. Velia (the  ancient "Elea" of Zeno and Parmenide)
is a hundred kilometers farther down along the coast.


GENERALITIES


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?

The school will address some of the above questions from a theoretical
and empirical view  point and will teach students  about this exciting
and very promising field using current  state of the art data sets for
pattern recognition, classification and regression.

The main  goals of the  school are: 

1.  Offer an overview of the  main research issues of ensemble methods
from the different and complementary perspectives of Machine Learning,
Neural Networks, Statistics and Pattern Recognition.

2.  Offer  theoretical tools  to analyze  the diverse  approaches, and
critically evaluate their applications.

3. Offer  practical  and theoretical  tools  to  develop new  ensemble
methods and analyze their application on real-world problems.



FORMAT

The  meeting will  follow  the  usual format  of  tutorials and  panel
discussions together  with poster sessions for  contributed papers.  A
demo  lab  with four  Linux  workstations  will  be available  to  the
participants for testing and comparing ensemble methods.

There will be a network of wireless 11MHz connection available so that
students  arriving  with their  laptops  and  an appropriate  wireless
communication card can stay connected while at the meeting area.


DURATION

Participants are  expected to arrive in  time for the  evening meal on
Sunday Sept 22th and depart on  Saturday Sept 28th. Sessions will take
place from Monday Sept 23th to Saturday Sept 28th.


PROCEEDINGS

The proceedings  will be  published in the  form of a  book containing
tutorial chapters written by the lecturers and possibly shorter papers
from other participants. One free copy of the book will be distributed
to each participant after the school.


LANGUAGE

The official language of the school will be English.


POSTER SUBMISSION

There  will be  a poster  session for  contributed  presentations from
participants. Proposals  consisting of a one page  abstract for review
by the organizers should be submitted with applications.


REGISTRATION FEE

Master and  PhD Students:              650,00 Euro
Academic Participants (govt/univ):     800,00 Euro
Industrial Participants:             1.100,00 Euro

The fee  includes accommodation (3  stars hotel - double  room), meals
and a  copy of the proceedings  of the school.   Transportation is not
included.
A supplement of 20 Euro per night should be paid for single room.
Members of sponsoring organizations will receive a discount of 50 Euro
on the registration fee.
A few scholarships are available for students who are otherwise unable
to participate at  the school.  Payment details will  be notified with
acceptance of applications.


ELIGIBILITY

The school is  open to all suitably qualified  scientists. People with
few years of  experience in the field should  include a recommendation
letter of their supervisor.


APPLICATION PROCEDURE

Important Dates:
       Application deadline:                          July   1,  2002
       Notification of acceptance:                  July  10, 2002
       Registration fee payment deadline:        July  20, 2002
       School                                  Sept  22-28 2002

Places are limited to a maximum  of 60 participants in addition to the
lecturers.  These will  be allocated  on  a first  come, first  served
basis.



**********************************************************************

                      APPLICATION FORM

Title: ...............................................................

Family Name: .........................................................

Other Names:..........................................................

Mailing Address (include institution or company name if appropriate):
.....................................................................
.....................................................................
.....................................................................
.....................................................................
.....................................................................

Phone:......................Fax:......................................

E-mail: ..............................................................

Date of Arrival : ....................................................

Date of Departure: ...................................................

Are  you  sending  the  abstract  of a  poster?   yes/no  (delete  the
alternative  which does not apply)

Are you  applying for a scholarship?  yes/no  (delete the alternative
which does not apply) If  yes please  include  a justification  letter  
for the  scholarship request.

*****************************************************************

Please send the application form together the recommendation letter
by electronic mail to:
iiass.vietri at tin.it, subject: summer school;
or
by fax to: +39 089 761 189 (att.ne Prof. M. Marinaro)
or
by ordinary mail to the address below:
                 IIASS
                 Via Pellegrino 19, 
                 I-84019 Vietri sul Mare (Sa) Italy


WEB PAGE OF THE COURSE

The web page of  the course is http://www.iiass.it/school2002 and will
contain all the updates related to the course.
At  http://www.iiass.it/school2002/ensemble-lab.html a  web  portal to
ENSEMBLE  METHODS is  in  development including  pointers to  relevant
papers,  data-bases and  software.  Contributions to  this portal  are
kindly requested to all researchers involved in this area. Please send
all contributions to Giorgio Valentini (valenti at disi.unige.it).


FOR FURTHER INFORMATION PLEASE CONTACT:

Prof. Francesco Masulli
DISI & INFM                                  email: masulli at ge.infm.it
Via Dodecaneso 35                            fax:   +39 010 353 6699
16146 Genova (Italy)                         tel:   +39 010 353 6604