**Previous message:**Ken Brown: "CFP: Constraints and Uncertainty Workshop"**Messages sorted by:**[ date ] [ thread ] [ subject ] [ author ]

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

Berkeley Initiative in Soft Computing (BISC)

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

==========================================

Dear Colleagues,

To ensure your messages to our mailing list will be distributed

efficiently please forward your messages or announcements with regards

to CFP (call for paper), conference, workshop, book announcement, etc.

as

follows:

1. Title

2. A short message in order of 2-3 sentence

3. Contact person's name and Email (address, tel, etc.)

4. URL for more detailed information.

5. Please avoid long messages if would be possible

Please note, we do post the messages weekly, except special BISC

announcements and viewpoints.

Best Regards,

Masoud Nikravesh

UC Berkeley

BISC program

==========================================

Call for papers for the special issue of the

International Journal of Applied Mathematics and Computer Science

on Computing with Words (CW).

__________________________________________________________________

Computing with words is an emerging methodology in which the

objets of computation are words or propositions in a natural language.

Basically, computing with words serves to harness the high expressive

power of natural languages to provide solutions to problems which lie

beyond the reach of conventional computational techniques. In so doing,

computing with words opens the door to a wide-ranging enlargement of

the role of natural languages in the basic and applied sciences, and

especially in information processing, decision and control.

Computing with words draws upon many fields which in one way or

another relate to reasoning and computation in the context of natural

languages. Among such fields are fuzzy logic, granular computting,

semantics of natural languages and computational linguistics.

Papers which relate to computing with words directly or indirectly

are invited for submission. The deadline for submission is October 15,

2001.

The notification of acceptance/rejection will be sent by December 15,

2001.The Special Issue is scheduled to appear in

mid-2002.

To facilitate the editorial process the interested authors are requested

to send a short e-mail message to the editors with a

tentative title of their intended contribution.

Guest Editors:

Danuta Rutkowska, E-mail: drutko@kik.pcz.czest.pl

Janusz Kacprzyk, E-mail: kacprzyk@ibspan.waw.pl

Lotfi A. Zadeh, E-mail: zadeh@cs.Berkeley.edu

============================================

You are kindly invited to participate with presentation at the session

"New

paradigms for understanding society" of the XVth World Congress of

Sociology (Brisbane, Australia, 7-13 July, 2002).

More information about the session you can find at

http://www.ucm.es/info/isa/congress2002/rc/rc51calls/rc51-05.htm

The deadline for a 500-1000 word abstract (sent by E-mail to me) is 31

August 2001.

Kind regards,

Vladimir

================================================

Dear Colleague

I would like to invite you to attend the

IEEE International Conference on Software Maintenance, 2001, and

associated workshops: IEEE SCAM, IEEE WESS, IEEE WSE, TABOO.

FLORENCE, ITALY, 6-10 November 2001

http://www.dsi.unifi.it/icsm2001

Sponsored by IEEE

Supported bt the: EC-IST, University of Florence, O-Groupi, IBM Italy

in collaboration with: TABOO, AICA, AIIA, ERCIM, UNINFO, CESVIT, ...

ICSM is the major international conference in the field of software and

systems maintenance, evolution, and management.

Theme: Systems and Software Evolution in the era of the Internet

kEYWORDS: software evolution, embedded suystems, program analysis,

reengineering, managment, maintenance, lyfe cycle, Internet and

distributed

systems, Multimedia systems, User interface evolution, Commercial

off-the-shelf

(COTS), Program comprehension, Formal methods, Empirical studies,

Testing and regression testing, Measurement of software, METRICS,etc.

Outstanding Keynotes such as:

Prof. David Lorge Parnas and Prof. Dieter Rombach. Kent Beck

110 technical presentations, 4 workshops,

Industrial papers and experiences, reseach papers and award, tutorials,

tool expositions, dissertation forum and award, workshops, panels,

and other exciting activities have been planned.

Please forward the following to anybody who you think may be interested.

The discount for the advanced registration fee will be active for few

weeks.

Apologies for multiple receptions.

If you would like to be removed from our list please send an email to

icsm2001@dsi.unifi.it with REMOVE in the subject.

Paolo Nesi

(ICSM2001 General Chair)

==================================================

CALL FOR PAPERS

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

We apologize if you receive multiple copies of this message.

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

Dear Colleagues,

We are organizing an exciting event: HIS'2001: International Workshop on

Hybrid Intelligent Systems in conjunction with The 14th Australian Joint

Conference on Artificial Intelligence (AI'01).

(Technically co-sponsored by The World Federation of Soft Computing)

HIS'01 is an International Workshop that brings together researchers,

developers, practitioners, and users of neural networks, fuzzy inference

systems, evolutionary algorithms and conventional techniques. The aim of

HIS'01 is to serve as a forum to present current and future research

work as

well as to exchange research ideas in this field.

HIS'01 invites authors to submit their original and unpublished work

that

demonstrate current research using hybrid computing techniques and their

applications in science, technology, business and commercial.

Topics of interest include but not limited to:

Applications / techniques using the following, but not limited to:

* Machine learning techniques (supervised/unsupervised/

reinforcement learning)

* Neural network and evolutionary Algorithms

* Neural networks and fuzzy inference systems

* Fuzzy clustering algorithms optimized using evolutionary

algorithms

* Evolutionary computation (genetic algorithms, genetic

programming ,evolution strategies, grammatical evolution etc.)

* Hybrid optimization techniques (simulated annealing, tabu search,

GRASP etc.)

* Hybrid computing using neural networks - fuzzy systems -

evolutionary algorithms

* Hybrid of soft computing and hard computing techniques

* Models using inductive logic programming, logic synthesis,

grammatical inference, case-based reasoning etc.

* Other intelligent techniques ( support vector machines,

rough sets, Bayesian networks, probabilistic reasoning,

minimum message length etc)

==============================================

In soft computing, intelligent control theory, and in "data mining,"

there

is a "simple" basic question which has been revisited again and again by

many people:

How can any system (brain or software...) learn to approximate a

nonlinear

mapping from a vector of inputs X to a vector of outputs Y, when given a

database (fixed or real-time) of examples of X and Y?

(One example: Shankar Shastri of Berkeley and his student Claire Tomlin

of

Stanford

have done excellent work in "hybrid control" -- which ends up requiring

a

general-purpose

nonlinear function approximator in the insides of the design. In fact,

it

is all quite close

to what we have done with approximate dynamic programming or

reinforcement

learning...

different words, different spins, but the same underlying mathemnatics.)

In neural networks, we call this the "supervised learning task." In

fuzzy

logic, Jim Bezdek

has called it "system identification." But in any case... one cannot

build

systems capable of

brain-like decision capability without a subsystem that can perform that

task (among others).

Thus I would argue that no model of learning in the brain could capture

the

higher abilities

of the brain, UNLESS it had enough richness to be able to handle this

simple task.

--------

Here is my concern: in the last few years, there has been a certain

amount

of drifting apart

between the computational neuroscience world and the world of

computational

intelligence.

Many people believe this is just fine... but what if the consequence is

that the neuroscience side

is dominated by models which cannot possibly explain the basic learning

capabilities of the system they

are studying? Perhaps there is a great need for some new mathematical

results which would explain

more clearly what the problem is, and encourage more interest in the

types

of model which can solve it.

(By the way, if anyone is interested... research on these lines would

fit

well as one of the many topics of

great interest to what we fund in computational intelligence...)

Moe precisely:

Most people on this list probably know already that many types of ANN

and

fuzzy system are "universal approximators,"

that they can learn any smooth mapping from X to Y.

Some of you may know about the very important results of Andrew Barron

(statistician at Yale), related to some results

of Sontag of Rutgers: he proved, in effect, that some universal

approximators are a lot better than others.

There are lots of simple "smoothed lookup table" approximators which

work

fine for low dimensions...

but the number of parameters or hidden units required grows

exponentially

with the number of inputs. But for multilayer perceptrons (MLPs)

the growth in complexity is only polynomial. This is an incredibly

important result. It says that MLPs may be viable

for large (brain-like) induction problems, while those others are not.

The

usual theorems for fuzzy logic approximation

and RBF approximation(and fuzzy ARTmap) are all based on some kind of

linear basis function argument, or someting very close to it,

which would imply an exponential growth in terms.

Now: DO THOSE RESULTS show that MLPS trained with backprop can perform

the

basic task of approximating at least smooth

nonlinear functions, and scaling up, while fuzzy and Hebbian systems

cannot? (If virtually all models now used in

computational neureoscience are of the Hebbian variety, continuous

(graded)

or discrete (spiking), this is serious...)

Not quite.

For example... when I think about Elastic Fuzzy Logic (ELF, first

published

by me in the IIzuka 1992 proceedings, pretty much equivalent to

parts of some of the later designs of Yager and Fukuda)....

it is clear that feedback to redefine the membership functions and so on

can achieve a lot better, more parsimonious

approximation ability than mere preset lookup tables! I would conjecture

that ELF

could also achieve Barron-like capability. And Cooper (of Nestor) has

played similar games

of tuning hidden units... long ago...

BUT: to achieve all this, one needs a FEEDBACK to train/select those

hidden

units!

Conjecture:

One may define a general concept of H-locality (Hebb-locality), similar

to

some of the rules Grossberg has

discussed, which prohibit both backpropagation and other similar types

of

feedback. The conjecture

is that ANY system made of simple units, whose learning must obey

H-locality, can never acheive

Barron-like polynomial scaling in approximating smooth functions. (

Technical point: **IF** one allows sneaky re-use

of weights, as in some of the multiplexing schemes Hecht-Nielsen has

talked

about, one can actually

implement backpropagation itself in networks which obey some of the

Grossberg rules -- but is this a plausible way to model

biological systems? Or do the biologists implicitly add further rules

which

rule out this kind of multiplexing?)

In any case, to prove or disprove this would be of ENORMOUS scientific

importance.

Proving it... would eliminate the main reason for not using GENERALIZED

backpropagation more in neuroscience models...

Disproving it would presumably lead to new Hebbian-style learning models

that could actually work on the difficult

kinds of tasks which engineers need to address.

More capable neuroscience models would of course make it much more

plausible for engineers to seriously consider trying to use/miomic such

models

in addressing difficult computational tasks.

Either way, the resolution of this issue will be important to bridging

the

deep divide between "machine learning" (in the largest

sense) and studies of learning in the brain.

Best of luck.

Paul W.

===================================================

--------------------------------------------------------------------

If you ever want to remove yourself from this mailing list,

you can send mail to <Majordomo@EECS.Berkeley.EDU> with the following

command in the body of your email message:

unsubscribe bisc-group

or from another account,

unsubscribe bisc-group <your_email_adress>

############################################################################

This message was posted through the fuzzy mailing list.

(1) To subscribe to this mailing list, send a message body of

"SUB FUZZY-MAIL myFirstName mySurname" to listproc@dbai.tuwien.ac.at

(2) To unsubscribe from this mailing list, send a message body of

"UNSUB FUZZY-MAIL" or "UNSUB FUZZY-MAIL yoursubscription@email.address.com"

to listproc@dbai.tuwien.ac.at

(3) To reach the human who maintains the list, send mail to

fuzzy-owner@dbai.tuwien.ac.at

(4) WWW access and other information on Fuzzy Sets and Logic see

http://www.dbai.tuwien.ac.at/ftp/mlowner/fuzzy-mail.info

(5) WWW archive: http://www.dbai.tuwien.ac.at/marchives/fuzzy-mail/index.html

**Next message:**J Lawry: "PhD studentships at Bristol"**Previous message:**Ken Brown: "CFP: Constraints and Uncertainty Workshop"**Messages sorted by:**[ date ] [ thread ] [ subject ] [ author ]

*
This archive was generated by hypermail 2b30
: Mon Jul 16 2001 - 13:14:41 MET DST
*