319() 2:20-3:10pm 101 (Prof S.Y. Kung)

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Nov 24, 2013 (3 years and 7 months ago)

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Training and Classification Efficiencies of


Kernel Methods:
From Regression Analysis to Machine Learning

時間:
3

19

(

) 2:20
-
3:10pm

地點:博理館

101
演講廳

講者:貢三元教授

(Prof S.Y. Kung)

Dept. of Electrical Engineering,

Princeton University

Abstract

Biography

Prof

S
.
Y
.

Kung

received

his

Ph
.
D
.

Degree

in

Electrical

Engineering

from

Stanford

University

in

1977
.

In

1974
,

he

was

an

Associate

Engineer

of

Amdahl

Corporation,

Sunnyvale,

CA
.

From

1977

to

1987
,

he

was

a

Professor

of

Electrical

Engineering
-
Systems

of

the

University

of

Southern

California,

L
.
A
.

Since

1987
,

he

has

been

a

Professor

of

Electrical

Engineering

at

the

Princeton

University
.

In

addition,

he

held

a

Visiting

Professorship

at

the

Stanford

University

(
1984
)
;

and

a

Visiting

Professorship

at

the

Delft

University

of

Technology

(
1984
)
;

a

Toshiba

Chair

Professorship

at

the

Waseda

University,

Japan

(
1984
)
;

an

Honorary

Professorship

at

the

Central

China

University

of

Science

and

Technology

(
1994
)
;

and

a

Distinguished

Chair

Professorship

at

the

Hong

Kong

Polytechnic

University

since

2001
.


His

research

interests

include

VLSI

array

processors,

system

modelling

and

identification,

neural

networks,

wireless

communication,

sensor

array

processing,

multimedia

signal

processing,

bioinformatic

data

mining

and

biometric

authentication
.

Prof
.

Kung

has

authored

more

than

400

technical

publications

and

numerous

textbooks,

Prof
.

Kung

has

co
-
authored

more

than

400

technical

publications

and

numerous

textbooks
.


Prof
.

Kung

is

a

Fellow

of

IEEE

since

1988
.

He

served

as

a

Member

of

the

Board

of

Governors

of

the

IEEE

Signal

Processing

Society

(
1989
-
1991
)
.

He

was

a

founding

member

of

several

Technical

Committees

(TC)

of

the

IEEE

Signal

Processing

Society

,

including

VLSI

Signal

Processing

TC

(
1984
),

Neural

Networks

for

Signal

Processing

TC

(
1991
)

and

Multimedia

Signal

Processing

TC

(
1998
),

and

was

appointed

as

the

first

Associate

Editor

in

VLSI

Area

(
1984
)

and

later

the

first

Associate

Editor

in

Neural

Network

(
1991
)

for

the

IEEE

Transactions

on

Signal

Processing
.

He

presently

serves

on

Technical

Committees

on

Multimedia

Signal

Processing
.


主辦單位:臺大電信所

協辦單位:臺大電信研究中心、中國電機工程學會

Green

machine

learning

technology

has

recently

become

a

critical

concern

for

many

IT

applications
.

It

is

important

to

address

practical

design

issues

concerning

the

kernel
-
based

approach

which

has

rapidly

become

the

mainstream

in

machine

learning
.

The

talk

will

start

with

regression

and

regularization

analysis

and


highlight

its

connection

to

supervised

classification

machine

learning
.

The

talk

will

address

several

system

design


aspects
:

training

time

during

the

learning

phase,

power

consumption

during

the

prediction

phase
;

robustness

and

degree

of

freedom

of

the

learned

system
;

and

model
-
induced

error

by

under
-
represented

kernels
.

It

will

be

demonstrated

that,

for

kernel
-
based

machine

learning,

a

vital

design

theme

is

to

strike

a

proper

balance

between

the

discriminating

strength

and

model

complexity
.

Furthermore,

a

proper

screening

of

the

training

dataset

will

be

instrumental

to

the

prediction

performance

of

the

learned

classifiers
.