CLASS-L Archives

May 2013

CLASS-L@LISTS.SUNYSB.EDU

Options: Use Monospaced Font
Show HTML Part by Default
Condense Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Message-ID:
Sender:
"Classification, clustering, and phylogeny estimation" <[log in to unmask]>
Subject:
From:
Soledad De Esteban Trivigno <[log in to unmask]>
Date:
Wed, 22 May 2013 11:56:05 +0200
Content-Type:
multipart/alternative; boundary="----=_Part_168465_239723491.1369216565332"
MIME-Version:
1.0
Reply-To:
"Classification, clustering, and phylogeny estimation" <[log in to unmask]>
Parts/Attachments:
text/plain (1948 bytes) , text/html (6 kB)
Dear colleagues:

Registration is open for the course CLASSIFICATION AND REGRESSION TREES AND
NEURAL NETWORKS WITH R - Second Edition.

INSTRUCTORS: Dr. Llorenç Badiella (UAB, Spain), Dr. Joan Valls  (Biomedical
Research Institute of Lleida, Spain) and Dr. Montserrat Martínez-Alonso
(Biomedical Research Institute of Lleida, Spain).

DATES: November 4-7, 2013; 24 teaching hours.

PLACE:  Premises of Sabadell of the Institut Català de Paleontologia Miquel
Crusafont,  Sabadell,  Barcelona (Spain).

Organized by: Transmitting Science and the Institut Catalá de Paleontologia
Miquel  Crusafont.

More information: http://www.transmittingscience.org/cart_with_r.htm or  writing
to [log in to unmask]

The main goal of the methods such as CART (Classification and Regression Trees),
is to model and predict one response variable explained by a set of dependent
variables. This methods can be particularly effective to model interactions
between explanatory variables. On the other hand, as a statistical model, a
neural network is based on linear and non-linear combinations of explanatory
variables that interact with other combinations to predict or explain an outcome
variable. Both CART and neural networks methods can provide good results to
explain or predict an outcome variable, particularly when the number of
interactions is important. Nevertheless, these techniques also tend to over-fit
the data and a validation of the models is required. ROC methods, including a
sensitivity/specificity analyses and/or external validations can be performed to
assess the consistency of these techniques. Applications cover a wide range of
problems, including species classification in biology, prediction of the
prognosis of a patient in biomedicine, etc.

With best regards

Soledad De Esteban Trivigno, PhD.























----------------------------------------------
CLASS-L list.
Instructions: http://www.classification-society.org/csna/lists.html#class-l


ATOM RSS1 RSS2