...
important and active field of all Machine Learning
research.
z
Special issues of Machine Learning Journal, and
Journal of Machine Learning Research.
z
Kernel Machines: large class of learning ... Modularity
z
Any kernel
-
based learning algorithm composed of two
modules:
–
A general purpose learning machine
–
A problem specific kernel function
z
Any K
-
B algorithm...
... ACL 2007 Demo and Poster Sessions, pages 57–60,
Prague, June 2007.
c
2007 Association for Computational Linguistics
Support Vector Machines for Query-focused Summarization trained and
evaluated ... (which happens in
2
The mean, median, standard deviation and histogram of the
overlapping distribution are calculated and included as features.
58
this case), and a simple iterative...
... The resulting vocabu-
lary consisted of 276 words and 56 POS tags.
4.3 Support Vector Machines
Support vector machines (SVMs) are a machine
learning technique used in a variety of text classi-
fication ... e.g. Lee and
Myaeng’s (2002) genre and subject detection work
and Boulis and Ostendorf’s (2005) work on feature
selection for topic classification.
For our LM classifiers,...
... support
vector learning for chunk identification. In Proceed-
ings of the 4th Conference on CoNLL-2000 and LLL-
2000, pages 142–144.
Taku Kudo and Yuji Matsumoto. 2001. Chunking with
support vector ... of support vec-
tor machines using sequential minimal optimization.
In Bernhard Sch¨olkopf, Christopher J.C. Burges, and
AlexanderJ. Smola, editors, Advances in Kernel Meth-...
...
support
vector
only
need
to
describe
the
data
with
known
category,
then
domain
classifer
(SVDC),
then
an
incremental
learning
obtaining
the
description
boundary
of
this
class
of
data.
algorithm
based
on
SVDC
was
proposed.
The
basic
idea
of
Finally,
we
can
classify
the
unknown
binary-class
data
this
incremental
algorithm
is
to
obtain
the
initial
target
according
to
the
obtained
boundary....
...
Σ
. . .
output σ (Σ υ
i
k (x,x
i
))
weights
υ
1
υ
2
υ
m
. . .
. . .
test vector x
support vectors x
1
x
n
mapped vectors Φ(x
i
), Φ(x)
Φ(x)
Φ(x
n
)
dot product (Φ(x)
.
Φ(x
i
)) = k (x,x
i
)
(