Publications

crazymeasleΤεχνίτη Νοημοσύνη και Ρομποτική

15 Οκτ 2013 (πριν από 4 χρόνια και 28 μέρες)

154 εμφανίσεις

Materi Pendukung : T0264P
25_2

Publications



Kirlin, P. B., & Utgoff, P. E. (2005). VoiSe: Learning to segregate voices in
explicit and implicit polyphony. In Reiss, J. D., & Wiggins, G. A. (Eds.),
Proceedings of the Sixth International Conference on Music I
nformation
Retrieval

(pp. 552
-
557). London: Queen Mary, University of London. (
letter
.ps.gz
) (
letter .pdf
) (
a4 .ps.gz
) (
a4 .pdf
)



Stracuzzi, D.J. (2005). Scalable knowledge acquisition through memory
organization.
International and Interdisciplinary Conference on Knowledge
Representation and Reasoning (AKRR 05)
. 57
-
64. Helsinki, Finland: Hel
sinki
University of Technology.
(.ps)

(.pdf)

Best Student Paper




Blaschko, M.B., Holness, G., Mattar, M.A., Lisin, D., Ut
goff, P.E., Hanson, A.R.,
Schultz, H.J., & Riseman, E.M. (in press). Automatic in situ identification of
plankton.
Workshop on Applications of Computer Vision
.



Stracuzzi, D.J., & Utgoff, P.E. (2004). Randomized variable elimination.
Journal
of Machine Lea
rning Research, 5
, 1331
-
1362.
(.ps)

(.pdf)




Stoddard, J., Raphael, C., & Utgoff, P.E. (2004). Well
-
tempered spelling: A key
-
invar
iant pitch spelling algorithm.
International Symposium on Music Information
Retrieval
.



Precup, D., & Utgoff, P.E. (2004). Classification using Phi
-
machines and
constructive function approximation.
Machine Learning, 55
, 31
-
52.



Utgoff, P.E., & Stracuzzi, D
.J. (2002). Many
-
layered learning.
Neural
Computation, 14
, 2497
-
2529.
(.ps)
,
(.pdf)




Stracuzzi, D.J., & Utgoff, P.E. (2002). Rand
omized variable elimination.
Proceedings of the Nineteenth International Conference on Machine Learning

(pp. 594
-
601). Sydney, Australia: Morgan Kaufmann.
(.ps)

(.pdf)




Utgoff, P.E., & Stracuzzi, D.J. (2002). Many
-
layered learning.
Proceedings of the
Second International Conference on Development and Learning

(pp. 141
-
146).
(.ps)

(.pdf)




Utgoff, P.E., & Cochran, R.P. (2001). A least
-
certainty heuristic for selective
search.
Proceedings of the Second International Conference on Computers an
d
Games

(pp. 1
-
18). Springer Verlag.
(.ps)
,
(.pdf)




Utgoff, P.E. (2001). Feature construction for game playing (pp. 131
-
1
52). In
Fuerenkranz & Kubat (Eds.),
Machines that learn to play games
. Nova Science
Publishers.
(.ps)
,
(.pdf)




Piater, J.H.,
Riseman, E.M., & Utgoff, P.E. (1999). Interactively training pixel
classifiers.
International Journal of Pattern Recognition and Artificial
Intelligence, 13
, 171
-
193.



Utgoff, P.E., & Stracuzzi, D.J. (1999). Approximation via value unification.
Proceedings

of the Sixteenth International Conference on Machine Learning

(pp.
425
-
432). Ljubljana: Morgan Kaufmann.
(.ps)
,
(.p
df)




Utgoff, P.E. (1998). Decision trees (pp. 222
-
224). In Wilson & Keil (Eds.),
The
MIT encyclopedia of cognitive sciences
. Bradford.
(.ps)
,
(.pdf)




Utgoff, P.E., & Cohen, P.R. (1998). Applicability of reinforcement learning.
Proceedings of the 1998 ICML Workshop on the Methodology of Applying
Machine Learning

(pp. 37
-
43). AAAI Press Report WS
-
98
-
16.



Utgoff, P.E., &

Precup, D. (1998). Constructive function approximation (pp. 219
-
235). In Liu & Motoda (Eds.),
Feature extraction, construction, and selection: A
data
-
mining perspective
. Kluwer.
(.ps)
,
(.pdf)




Moss, J.E.B., Utgoff, P.E., Cavazos, J., Precup, D., Stefanovic, D., Brodley, C., &
Scheeff, D. (1998). Learning to schedule straight
-
line code.
Advances in Neural
Information Proce
ssing Systems

(pp. 929
-
935). San Mateo, CA: Morgan
Kaufmann.



Piater, J., Riseman, E., & Utgoff, P.E. (1998). Interactively training pixel
classifiers.
Eleventh International FLAIRS Conference (FLAIRS
-
98)

(pp. 57
-
61).



Precup, D., & Utgoff, P.E. (1998). Cl
assification using phi
-
machines and
constructive function approximation.
Proceedings of the Fifteenth International
Conference on Machine Learning

(pp. 439
-
444).



Schmill, M.D., Rosenstein, M.T., Cohen, P.R., & Utgoff, P.E. (1998). Learning
what is relevan
t to the effects of actions for a mobile robot.
Proceedings of the
Second International Conference on Autonomous Agents

(pp. 247
-
253).



Utgoff, P.E., Berkman, N.C., & Clouse, J.A. (1997). Decision tree induction
based on efficient tree restructuring.
Machi
ne Learning, 29
, 5
-
44.
(.ps)
,
(.pdf)




Clouse, J.A. (1996). The role of training in reinforcement learning. In Donahoe
(Ed.),
Neural

Network Models of Cognition: Biobehavioral Foundations
.
Amsterdam: Elsevier Science Publishers.



Brodley, C.E., & Utgoff, P.E. (1995). Multivariate decision trees.
Machine
Learning, 19
, 45
-
77.



Brodley, C.E. (1995). Recursive automatic bias selection for
classifier
construction.
Machine Learning, 20
, 63
-
94.



Clouse, J.A. (1995). Learning from an automated training agent.
Proceedings:
ML95 Workshop on `Agents that Learn from Other Agents'
.
(
\
verb@http://www.cs.wisc.edu/shavlik/ml95w1/@.)



Brodley, C.E., & U
tgoff, P.E. (1994). Dynamic recursive model class selection
for classifier construction. In Cheeseman & Oldford (Eds.),
Selecting Models
from Data: Artificial Intelligence and Statistics IV
. New York: Springer
-
Verlag.



Draper, B.A., Brodley, C.E., & Utgoff
, P.E. (1994). Goal
-
directed classification
using linear machine decision trees.
IEEE Transactions on Pattern Analysis and
Machine Intelligence, 16
, 888
-
893. (Special Issue on Vision and Machine
Learning)



Utgoff, P.E. (1994). An improved algorithm for inc
remental induction of decision
trees.
Machine Learning: Proceedings of the Eleventh International Conference

(pp. 318
-
325). New Brunswick, NJ: Morgan Kaufmann.



Brodley, C.E., & Utgoff, P.E. (1993). Dynamic recursive model class selection
for classifier co
nstruction.
Preliminary Papers of the Fourth International
Workshop on Artificial Intelligence and Statistics

(pp. 179
-
184).



Brodley, C.E. (1993). Addressing the selective superiority problem: Automatic
algorithm/model class selection.
Machine Learning: P
roceedings of the Tenth
International Conference

(pp. 17
-
24). Amherst, MA: Morgan Kaufmann.



Fawcett, T.E., & Utgoff, P.E. (1992). Automatic feature generation for problem
solving systems.
Machine Learning: Proceedings of the Ninth International
Conference

(pp. 144
-
153). San Mateo, CA: Morgan Kaufmann.



Clouse, J.A., & Utgoff, P.E. (1992). A teaching method for reinforcement
learning.
Machine Learning: Proceedings of the Ninth International Conference

(pp. 92
-
101). San Mateo, CA: Morgan Kaufmann.



Callan, J
.P., & Utgoff, P.E. (1991). Constructive induction on domain knowledge.
Proceedings of the Ninth National Conference on Artificial Intelligence

(pp. 614
-
619). Anaheim, CA: MIT Press.



Callan, J.P., & Utgoff, P.E. (1991). A transformational approach to cons
tructive
induction.
Machine Learning: Proceedings of the Eighth International Workshop

(pp. 122
-
126). Evanston, IL: Morgan Kaufmann.



Callan, J.P., Fawcett, T.E., & Rissland, E.L. (1991). CABOT: An adaptive
approach to case
-
based search.
Proceedings of the

Twelfth International Joint
Conference on Artificial Intelligence

(pp. 803
-
808). Sidney, Australia: Morgan
Kaufmann.



Callan, J.P., Fawcett, T.E., & Rissland, E.L. (1991). Adaptive case
-
based
reasoning.
Proceedings of the DARPA Workshop on Case
-
Based Reas
oning

(pp.
179
-
190). Washington, D.C.: Morgan Kaufmann.



Fawcett, T.E., & Utgoff, P.E. (1991). A hybrid method for feature generation.
Machine Learning: Proceedings of the Eighth International Workshop

(pp. 137
-
141). Evanston, IL: Morgan Kaufmann.



Saxena,

S. (1991). On the effect of instance representation on generalization.
Machine Learning: Proceedings of the Eighth International Workshop
. Evanston,
IL: Morgan Kaufmann.



Utgoff, P.E., & Clouse, J.A. (1991). Two kinds of training information for
evaluatio
n function learning.
Proceedings of the Ninth National Conference on
Artificial Intelligence

(pp. 596
-
600). Anaheim, CA: MIT Press.
(.ps)
,
(.pdf)




Saxena, S. (1990). Using description length to evaluate input representations for
learning.
Proceedings of the AAAI Spring Symposium on the Theory and
Application of Minimal Length Encoding

(pp. 135
-
139).



Utgoff, P.E., & Brodley, C.E. (199
0). An incremental method for finding
multivariate splits for decision trees.
Proceedings of the Seventh International
Conference on Machine Learning

(pp. 58
-
65). Austin, TX: Morgan Kaufmann.
(.ps)
,
(.pdf)




Yee, R.C., Saxena, S., Utgoff, P.E., & Barto, A.G. (1990). Explaining temporal
-
differences to create useful concepts for evaluating states.
Proceedings of the
Eighth Na
tional Conference on Artificial Intelligence
. Boston, MA: Morgan
Kaufmann.



Callan, J.P. (1989). Knowledge
-
based feature generation.
Proceedings of the Sixth
International Workshop on Machine Learning

(pp. 441
-
443). Ithaca, NY: Morgan
Kaufmann.



Fawcett, T
. (1989). Learning from plausible explanations.
Proceedings of the
Sixth International Workshop on Machine Learning

(pp. 37
-
39). Ithaca, NY:
Morgan Kaufmann.



Saxena, S. (1989). Evaluating alternative instance representations.
Proceedings of
the Sixth Inte
rnational Workshop on Machine Learning

(pp. 465
-
468). Ithaca,
NY: Morgan Kaufmann.



Utgoff, P.E. (1989). Improved training via incremental learning.
Proceedings of
the Sixth International Workshop on Machine Learning
. Ithaca, NY: Morgan
Kaufmann.



Utgoff,
P.E. (1989). Incremental induction of decision trees.
Machine Learning,
4
, 161
-
186.
(.ps)
,
(.pdf)




Utgoff, P.E. (1989). Perceptro
n trees: A case study in hybrid concept
representations.
Connection Science, 1
, 377
-
391.



Utgoff, P.E. (1988). ID5: An incremental ID3.
Proceedings of the Fifth
International Conference on Machine Learning

(pp. 107
-
120). Ann Arbor, MI:
Morgan Kaufman.



Utg
off, P.E. (1988). Perceptron trees: A case study in hybrid concept
representations.
Proceedings of the Seventh National Conference on Artificial
Intelligence

(pp. 601
-
606). Saint Paul, MN: Morgan Kaufmann.



Utgoff, P.E., & Heitman, P.S. (1988). Learning an
d generalizing move selection
preferences.
Proceedings of the AAAI Symposium on Computer Game Playing

(pp. 36
-
40). Palo Alto, CA.



Utgoff, P.E., & Saxena, S. (1988). Obtaining efficient classifiers from
explanations.
Proceedings of the AAAI Symposium on Ex
planation Based
Learning

(pp. 47
-
51). Palo Alto, CA.



Connell, Margaret E., & Utgoff, Paul E. (1987). Learning to control a dynamical
system.
Proceedings of the Sixth National Conference on Artificial Intelligence

(pp. 456
-
460). Seattle, WA: Morgan Kaufman
n.



Connell, Margaret, E., & Utgoff, Paul E. (1987). Learning to control a dynamical
physical system.
Computational Intelligence, 3
, 330
-
337.



Utgoff, P.E., & Saxena, S. (1987). Learning a preference predicate.
Proceedings
of the Fourth International Works
hop on Machine Learning

(pp. 115
-
121). Irvine,
CA: Morgan Kaufmann.



Utgoff, P.E. (1986).
Machine learning of inductive bias
. Hingham, MA: Kluwer.
(reviewed in IEEE Expert, Fall 1986)

Unpublished Reports



Utgoff, P.E., Raphael, C., & Stoddard, J. (2004).
D
etecting motives and recurring
patterns in polyphonic music
, (Technical Report 04
-
31), Amherst, MA:
University of Massachusetts, Computer Science Department.



Utgoff, P.E., Ding, G., & Riseman, E.R. (2003).
Feature sets for texture
classification
, (03
-
38),

Amherst, MA: University of Massachusetts, Computer
Science Department.



Utgoff, P.E., Jensen, D., & Lesser, V. (2000).
Inferring task structure from data
,
(Technical Report TR
-
00
-
54), Amherst, MA: University of Massachusetts,
Computer Science.



Stracuzzi,

D.J., & Utgoff, P.E. (2000).
Feature compilation
, (TR
-
00
-
18),
Amherst, MA: University of Massachusetts, Computer Science Department.



Utgoff, P.E., & Qian, J. (1999).
A new polynomial function approximation
algorithm
, (Technical Report TR
-
99
-
20), Amherst,

MA: University of
Massachusetts, Computer Science.



Utgoff, P.E., & Precup, D. (1997).
Relative value function approximation
,
(Technical Report 97
-
03), Amherst, MA: University of Massachusetts,
Department of Computer Science.



Utgoff, P.E., & Precup, D. (
1997).
Constructive function approximation
,
(Technical Report 97
-
04), Amherst, MA: University of Massachusetts,
Department of Computer Science.



Clouse, J.A. (1997).
On in
tegrating apprentice learning and reinforcement
learning
, Doctoral Dissertation (Technical Report 97
-
26), Amherst, MA:
University of Massachusetts, Department of Computer Science.



Utgoff, P.E., & Clouse, J.A. (1996).
A Kolmogorov
-
Smirnoff metric for deci
sion
tree induction
, (Technical Report 96
-
3), Amherst, MA: University of
Massachusetts, Department of Computer Science.



Utgoff, P.E. (1996).
ELF: An evaluation function learner that constructs its own
features
, (Technical Report 96
-
65), Amherst, MA: Unive
rsity of Massachusetts,
Department of Computer Science.



Clouse, J.A. (1996).
An introspection approach to querying a trainer
, (Technical
Report 96
-
13), Amherst, MA: University of Massachusetts, Department of
Computer Science.



Utgoff, P.E. (1995).
Interne
t program competition
, (Technical Report 95
-
67),
Amherst, MA: University of Massachusetts, Department of Computer Science.



Clouse, J.A. (1995).
On training automated agents
, (Technical Report 95
-
109),
Amherst, MA: University of Massachusetts, Computer Sci
ence Department.



Clouse, J.A. (1995).
Action set approach to reinforcement learning
, (Technical
Report 95
-
108), Amherst, MA: University of Massachusetts, Computer Science
Department.



Berkman, N.C. (1995).
Value grouping for binary decision trees
, (Techni
cal
Report 95
-
19), Amherst, MA: University of Massachusetts, Department of
Computer Science.



Berkman, N.C., & Sandholm, T.W. (1995).
What should be minimized in a
decision tree: A re
-
examination
, (Technical Report 95
-
20), Amherst, MA:
University of Massac
husetts, Department of Computer Science.



Callan, J.P. (1993).
Knowledge
-
based feature generation for inductive learning
.
Doctoral dissertation, Department of Computer Science, University of
Massachusetts, Amherst, MA.



Fawcett, Tom E. (1993).
Feature disc
overy for problem solving systems
. Doctoral
dissertation, Department of Computer Science, University of Massachusetts,
Amherst, MA.



Clouse, J.A. (1992).
Learning application coefficients with a Sigma
-
Pi unit
.
Master's thesis, Computer Science Department,
University of Massachusetts,
Amherst, MA.



Saxena, S. (1991).
Predicting the effect of instance representations on inductive
learning
. Doctoral dissertation, Department of Computer Science, University of
Massachusetts, Amherst, MA.



Utgoff, P.E., & Brodley
, C.E. (1991).
Linear machine decision trees
, (COINS
Technical Report 91
-
10), Amherst, MA: University of Massachusetts, Department
of Computer and Information Science.
(.ps)
,
(.pdf)




Saxena, S., & Utgoff, P.E. (1990).
A new set cover heuristic
, (TR
-
90
-
5), Amherst,
MA: University of Massachusetts, Computer and Information Science
Department.



Saxena, S., & Utgoff, P.E. (1
988).
A relationship between classification accuracy
and search quality
, (Coins Technical Report 88
-
104), Amherst, MA: University
of Massachusetts, Department of Computer and Information Science.



Utgoff, P.E., & Saxena, S. (1987).
A perfect lookup table e
valuation function for
the eight
-
puzzle
, (COINS Technical Report 87
-
71), Amherst, MA: University of
Massachusetts, Department of Computer and Information Science.