Loading
Error: Cannot Load Popup Box
Skip to hit list
Adjust your hit list
Further result pages
Mobile

A
A
A

A

English
Deutsch
Français
Español
Polski
Ελληνικά
Українська
中文
 Logged in as

Log Out

Login
BASIC
SEARCH
ADVANCED
SEARCH
HELP
BROWSING
SEARCH
HISTORY
Your search
Search For:
Entire Document
Title
Author
Subject
Boost open access documents
Find
Linguistics tools
Verbatim search
Additional word forms
Multilingual synonyms
Statistics
26 hits
in 72,045,933 documents
in 0.32 seconds
Please leave the following field blank:
Home
»
Search: V. Tresp
Hit List
Hit list
1.
ModelIndependent Mean Field Theory as a Local Method for Approximate Propagation of Information
Open Access
Title:
ModelIndependent Mean Field Theory as a Local Method for Approximate Propagation of Information
Author:
M. Haft
;
R. Hofmann
;
V. Tresp
M. Haft
;
R. Hofmann
;
V. Tresp
Minimize authors
Description:
We present a systematic approach to mean field theory (MFT) in a general probabilistic setting without assuming a particular model and avoiding physical notation. The mean field equations derived here may serve as a local and thus very simple method for approximate inference in graphical models. In general, there are multiple solutions to the me...
We present a systematic approach to mean field theory (MFT) in a general probabilistic setting without assuming a particular model and avoiding physical notation. The mean field equations derived here may serve as a local and thus very simple method for approximate inference in graphical models. In general, there are multiple solutions to the mean field equations. We show that improved estimates can be obtained by forming a weighted mixture of the multiple mean field solutions. We derive simple approximate expressions for the mixture weights, which can also be obtained by means of only local computations. The benefits of taking into account multiple solutions are demonstrated by using MFT for inference in a small `noisyor network'. 1 Introduction The benefits of using a probabilistic setting in many applied fields where uncertainty plays a prominent role such as image processing, neural networks and artificial intelligence have become increasingly apparent [1]. Unfortunately, p.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090412
Source:
http://www7.informatik.tumuenchen.de/~hofmannr/mf.ps.gz
http://www7.informatik.tumuenchen.de/~hofmannr/mf.ps.gz
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.7205
http://www7.informatik.tumuenchen.de/~hofmannr/mf.ps.gz
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.7205
http://www7.informatik.tumuenchen.de/~hofmannr/mf.ps.gz
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
2.
ModelIndependent Mean Field Theory as a Local Method for Approximate Propagation of . . .
Open Access
Title:
ModelIndependent Mean Field Theory as a Local Method for Approximate Propagation of . . .
Author:
M. Haft
;
R. Hofmann
;
V. Tresp
M. Haft
;
R. Hofmann
;
V. Tresp
Minimize authors
Description:
We present a systematic approach to mean field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean field equations derived here may serve as a local and thus very simple method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. “Modelindependent” means tha...
We present a systematic approach to mean field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean field equations derived here may serve as a local and thus very simple method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. “Modelindependent” means that we do not assume a particular type of dependencies; in a Bayesian network, for example, we allow arbitrary tables to specify conditional dependencies. In general, there are multiple solutions to the mean field equations. We show that improved estimates can be obtained by forming a weighted mixture of the multiple mean field solutions. Simple approximate expressions for the mixture weights are given. The general formalism derived so far is evaluated for the special case of Bayesian networks. The benefits of taking into account multiple solutions are demonstrated by using MFT for inference in a small and in a very large Bayesian network. The results are compared to the exact results.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20101206
Source:
http://wwwbrauer.informatik.tumuenchen.de/~
tresp
vol/papers/generalMFT.pdf
http://wwwbrauer.informatik.tumuenchen.de/~
tresp
vol/papers/generalMFT.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
1
1
Minimize
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.1481
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/generalMFT.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.1481
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/generalMFT.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
3.
Mean Field Inference in a General Probabilistic Setting
Open Access
Title:
Mean Field Inference in a General Probabilistic Setting
Author:
M. Haft
;
R. Hofmann
;
V. Tresp
M. Haft
;
R. Hofmann
;
V. Tresp
Minimize authors
Description:
We present a systematic, modelindependent formulation of mean field theory (MFT) as an inference method in probabilistic models. "Modelindependent" means that we do not assume a particular type of dependency among the variables of a domain but instead work in a general probabilistic setting. In a Bayesian network, for example, you may use arbi...
We present a systematic, modelindependent formulation of mean field theory (MFT) as an inference method in probabilistic models. "Modelindependent" means that we do not assume a particular type of dependency among the variables of a domain but instead work in a general probabilistic setting. In a Bayesian network, for example, you may use arbitrary tables to specify conditional dependencies and thus run MFT in any Bayesian network. Furthermore, the general mean field equations derived here shed a light on the essence of MFT. MFT can be interpreted as a local iteration scheme which relaxes in a consistent state (a solution of the mean field equations). Iterating the mean field equations means propagating information through the network. In general, however, there are multiple solutions to the mean field equations. We show that improved approximations can be obtained by forming a weighted mixture of the multiple mean field solutions. Simple approximate expressions for the mixture weig.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090413
Source:
ftp://flop.informatik.tumuenchen.de/pub/hofmannr/mf27.ps.gz
ftp://flop.informatik.tumuenchen.de/pub/hofmannr/mf27.ps.gz
Minimize
Document Type:
text
Language:
en
DDC:
531 Classical mechanics; solid mechanics
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.5583
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.5583
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
4.
ModelIndependent Mean Field Theory as a Local Method for Approximate Propagation of Information
Open Access
Title:
ModelIndependent Mean Field Theory as a Local Method for Approximate Propagation of Information
Author:
M. Haft
;
R. Hofmann
;
V. Tresp
M. Haft
;
R. Hofmann
;
V. Tresp
Minimize authors
Description:
We present a systematic approach to mean field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean field equations derived here may serve as a local and thus very simple method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. "Modelindependent" means tha...
We present a systematic approach to mean field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean field equations derived here may serve as a local and thus very simple method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. "Modelindependent" means that we do not assume a particular type of dependencies; in a Bayesian network, for example, we allow arbitrary tables to specify conditional dependencies. In general, there are multiple solutions to the mean field equations. We show that improved estimates can be obtained by forming a weighted mixture of the multiple mean field solutions. Simple approximate expressions for the mixture weights are given. The general formalism derived so far is evaluated for the special case of Bayesian networks. The benefits of taking into account multiple solutions are demonstrated by using MFT for inference in a small and in a very large Bayesian network. The results are compared to the exact results.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090416
Source:
http://
tresp
.org/./papers/generalMFT.ps.gz
http://
tresp
.org/./papers/generalMFT.ps.gz
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.9776
http://tresp.org/./papers/generalMFT.ps.gz
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.9776
http://tresp.org/./papers/generalMFT.ps.gz
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
5.
normalization in the auditory system
Open Access
Title:
normalization in the auditory system
Author:
T K Leen
;
T G Dietterich
;
V Tresp
;
Odelia Schwartz
;
Eero P. Simoncelli
T K Leen
;
T G Dietterich
;
V Tresp
;
Odelia Schwartz
;
Eero P. Simoncelli
Minimize authors
Description:
Natural sound statistics and divisive
Natural sound statistics and divisive
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20080701
Source:
http://www.cns.nyu.edu/pub/lcv/schwartz00a.pdf
http://www.cns.nyu.edu/pub/lcv/schwartz00a.pdf
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.5254
http://www.cns.nyu.edu/pub/lcv/schwartz00a.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.5254
http://www.cns.nyu.edu/pub/lcv/schwartz00a.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
6.
Natural Sound Statistics and Divisive Normalization in the Auditory System
Open Access
Title:
Natural Sound Statistics and Divisive Normalization in the Auditory System
Author:
Odelia Schwartz
;
Eero P. Simoncelli
;
T K Leen
;
T G Dietterich
;
V Tresp
Odelia Schwartz
;
Eero P. Simoncelli
;
T K Leen
;
T G Dietterich
;
V Tresp
Minimize authors
Description:
We explore the statistical properties of natural sound stimuli preprocessed with a bank of linear filters. The responses of such filters exhibit a striking form of statistical dependency, in which the response variance of each filter grows with the response amplitude of filters tuned for nearby frequencies. These dependencies may be substantiall...
We explore the statistical properties of natural sound stimuli preprocessed with a bank of linear filters. The responses of such filters exhibit a striking form of statistical dependency, in which the response variance of each filter grows with the response amplitude of filters tuned for nearby frequencies. These dependencies may be substantially reduced using an operation known as divisive normalization, in which the response of each filter is divided by a weighted sum of the rectified responses of other filters. The weights may be chosen to maximize the independence of the normalized responses for an ensemble of natural sounds. We demonstrate that the resulting model accounts for nonlinearities in the response characteristics of the auditory nerve, by comparing model simulations to electrophysiological recordings. In previous work (NIPS, 1998) we demonstrated that an analogous model derived from the statistics of natural images accounts for nonlinear properties of neur.
Minimize
Publisher:
MIT Press
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090415
Source:
http://www.cns.nyu.edu/pub/eero/schwartz00a.ps.gz
http://www.cns.nyu.edu/pub/eero/schwartz00a.ps.gz
Minimize
Document Type:
text
Language:
en
DDC:
612 Human physiology
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.2430
http://www.cns.nyu.edu/pub/eero/schwartz00a.ps.gz
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.2430
http://www.cns.nyu.edu/pub/eero/schwartz00a.ps.gz
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
7.
DOI 10.1007/s109940105211x Statistical relational learning of trust
Open Access
Title:
DOI 10.1007/s109940105211x Statistical relational learning of trust
Author:
Mach Learn
;
Achim Rettinger
;
Matthias Nickles
;
Volker Tresp
;
Samuel Kaski
;
Jennifer Neville
;
Stefan Wrobel
;
A. Rettinger
;
M. Nickles
;
V. Tresp
Mach Learn
;
Achim Rettinger
;
Matthias Nickles
;
Volker Tresp
;
Samuel Kaski
;
Jennifer Neville
;
Stefan Wrobel
;
A. Rettinger
;
M. Nickles
;
V. Tresp
Minimize authors
Description:
Abstract The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentallyopaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between th...
Abstract The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentallyopaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn contextsensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on userratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trustsituation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic twoplayer negotiation scenario.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20110404
Source:
http://wwwbrauer.informatik.tumuenchen.de/%7Etrespvol/papers/fulltext.pdf
http://wwwbrauer.informatik.tumuenchen.de/%7Etrespvol/papers/fulltext.pdf
Minimize
Document Type:
text
Language:
en
DDC:
006 Special computer methods
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.186.1340
http://wwwbrauer.informatik.tumuenchen.de/%7Etrespvol/papers/fulltext.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.186.1340
http://wwwbrauer.informatik.tumuenchen.de/%7Etrespvol/papers/fulltext.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
8.
DOI 10.1007/s109940105211x Statistical relational learning of trust
Open Access
Title:
DOI 10.1007/s109940105211x Statistical relational learning of trust
Author:
Achim Rettinger
;
Matthias Nickles
;
Volker Tresp
;
Samuel Kaski
;
Jennifer Neville
;
Stefan Wrobel
;
A. Rettinger
;
M. Nickles
;
V. Tresp
Achim Rettinger
;
Matthias Nickles
;
Volker Tresp
;
Samuel Kaski
;
Jennifer Neville
;
Stefan Wrobel
;
A. Rettinger
;
M. Nickles
;
V. Tresp
Minimize authors
Description:
Abstract The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentallyopaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between th...
Abstract The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentallyopaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn contextsensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on userratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trustsituation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic twoplayer negotiation scenario.
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20130717
Source:
http://wwwbrauer.in.tum.de/~
tresp
vol/papers/10.1007_s109940105211x.pdf
http://wwwbrauer.in.tum.de/~
tresp
vol/papers/10.1007_s109940105211x.pdf
Minimize
Document Type:
text
Language:
en
DDC:
006 Special computer methods
(computed)
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.319
http://wwwbrauer.in.tum.de/~trespvol/papers/10.1007_s109940105211x.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.319
http://wwwbrauer.in.tum.de/~trespvol/papers/10.1007_s109940105211x.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
9.
TreeBased Modeling and Estimation of Gaussian Processes on Graphs with Cycles
Open Access
Title:
TreeBased Modeling and Estimation of Gaussian Processes on Graphs with Cycles
Author:
Martin J. Wainwright
;
Erik B. Sudderth
;
Alan S. Willsky
;
T. K. Leen
;
T. G. Dietterich
;
V. Tresp
Martin J. Wainwright
;
Erik B. Sudderth
;
Alan S. Willsky
;
T. K. Leen
;
T. G. Dietterich
;
V. Tresp
Minimize authors
Description:
We present the embedded trees algorithm, an iterative technique for estimation of Gaussian processes defined on arbitrary graphs. By exactly solving a series of modified problems on embedded spanning trees, it computes the conditional means with an efficiency comparable to or better than other techniques. Unlike other methods, the embedded trees...
We present the embedded trees algorithm, an iterative technique for estimation of Gaussian processes defined on arbitrary graphs. By exactly solving a series of modified problems on embedded spanning trees, it computes the conditional means with an efficiency comparable to or better than other techniques. Unlike other methods, the embedded trees algorithm also computes exact error covariances. The error covariance computation is most efficient for graphs in which removing a small number of edges reveals an embedded tree. In this context, we demonstrate that sparse loopy graphs can provide a significant increase in modeling power relative to trees, with only a minor increase in estimation complexity. 1
Minimize
Publisher:
MIT Press
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090415
Source:
http://ssg.mit.edu/~mjwain/ETalg_nips00_withheader.ps.gz
http://ssg.mit.edu/~mjwain/ETalg_nips00_withheader.ps.gz
Minimize
Document Type:
text
Language:
en
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.2861
http://ssg.mit.edu/~mjwain/ETalg_nips00_withheader.ps.gz
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.2861
http://ssg.mit.edu/~mjwain/ETalg_nips00_withheader.ps.gz
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
10.
Theory and practice, ” in Proc. 17th Int. Conf. Algorithmic Learn. Theory, 2006, pp. 319–333.
Open Access
Title:
Theory and practice, ” in Proc. 17th Int. Conf. Algorithmic Learn. Theory, 2006, pp. 319–333.
Author:
S. Kramer
;
G. Widmer
;
B. Pfahringer
;
M. Degroeve
;
R. Herbrich
;
T. Graepel
;
K. Obermayer
;
Lrage Margin Rank
;
V. E. Johnson
;
J. H. Albert
;
...
S. Kramer
;
G. Widmer
;
B. Pfahringer
;
M. Degroeve
;
R. Herbrich
;
T. Graepel
;
K. Obermayer
;
Lrage Margin Rank
;
V. E. Johnson
;
J. H. Albert
;
Ordinal Data Modeling (statistics For
;
A. Shashua
;
A. Levin
;
M. Almeida
;
A. Braga
;
J. Braga
;
Svmkm Speeding Svms
;
Z. Xu
;
K. Yu
;
V. Tresp
;
X. Xu
;
J. Wang
;
Representative Sampling
Minimize authors
Description:
[9] H. Lin and L. Li, “Largemargin thresholded ensembles for ordinal regression:
[9] H. Lin and L. Li, “Largemargin thresholded ensembles for ordinal regression:
Minimize
Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20130725
Source:
http://dollar.biz.uiowa.edu/%7Estreet/fayed09.pdf
http://dollar.biz.uiowa.edu/%7Estreet/fayed09.pdf
Minimize
Document Type:
text
Language:
en
Subjects:
data sets using support cluster machines ; ” in Proc. 14th Annu
data sets using support cluster machines ; ” in Proc. 14th Annu
Minimize
Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Minimize
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.5257
http://dollar.biz.uiowa.edu/%7Estreet/fayed09.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.5257
http://dollar.biz.uiowa.edu/%7Estreet/fayed09.pdf
Minimize
Content Provider:
CiteSeerX
My Lists:
My Tags:
Notes:
Detail View
Email this
Export Record
Export Record
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Add to Favorites
Check in Google Scholar
Add to another List
Edit Favorit
Delete from Favorites
Export Record
All Records
Export
» RefWorks
» EndNote
» RIS
» BibTeX
» MARC
» RDF
» RTF
» JSON
» YAML
Adjust your hit list
Sort Your Results
Refine Search Result
More Options
Sort Your Results
Sort by:
Relevance
Author, ZA
Author, AZ
Title, AZ
Title, ZA
Date of publication, descending
Date of publication, ascending
Refine Search Result
Author
(13) Tresp, V
(11) Dietterich, TG
(10) Leen, TK
(10) The Pennsylvania State University CiteSeerX...
(8) V. Tresp
(5) Dayan, P
(4) M. Haft
(4) R. Hofmann
(3) Tresp, V.
(2) A. Rettinger
(2) Achim Rettinger
(2) Eero P. Simoncelli
(2) Ghahramani, Z
(2) Hinton, GE
(2) Jennifer Neville
(2) Kakade, S
(2) Leen, T
(2) M. Nickles
(2) Matthias Nickles
(2) Odelia Schwartz
(2) Samuel Kaski
(2) Stefan Wrobel
(2) T G Dietterich
(2) T K Leen
(2) V Tresp
(2) Volker Tresp
(1) A. Braga
(1) A. Levin
(1) A. Shashua
(1) Alan S. Willsky
(1) B. Pfahringer
(1) Beal, MJ
(1) Bengio, Samy
(1) Bourlard, Hervé
(1) Briegel, T.
(1) Chapelle, O
(1) Cristianini, N
(1) Dieterich, T
(1) Dietterich, T.
(1) Dietterich, T. G.
(1) Erik B. Sudderth
(1) G. Widmer
(1) Hertz, J
(1) Hindriks, K
(1) J. Braga
(1) J. H. Albert
(1) J. Wang
(1) K. Obermayer
(1) K. Yu
(1) Kali, S
(1) Klusch, M
(1) Leen, T.
(1) Leen, T. K.
(1) Li, Z
(1) Li, ZP
(1) Lodhi, H
(1) Lrage Margin Rank
(1) M. Almeida
(1) M. Degroeve
(1) Mach Learn
(1) Martin J. Wainwright
(1) Mukherjee, S
(1) Nickles, M
(1) Ordinal Data Modeling (statistics For
(1) Papazoglou, M P
(1) Poggio, T
(1) Pontil, M
(1) R. Herbrich
(1) Rasmussen, CE
(1) Representative Sampling
(1) Rettinger, A
(1) S. Kramer
(1) Sallans, B
(1) Scarpetta, S
(1) Seeger, Matthias
(1) ShaweTaylor, J
(1) Strerling, L
(1) Svmkm Speeding Svms
(1) T. G. Dietterich
(1) T. Graepel
(1) T. K. Leen
(1) Teh, YW
(1) V. E. Johnson
(1) Vapnik, V
(1) Watkins, CJCH
(1) Weber, Katrin
(1) Weston, J
(1) Williams, Christopher
(1) X. Xu
(1) Z. Xu
Author:
Subject
(2) models
(1) 1
(1) amnesia
(1) backward blocking
(1) cortex
(1) data sets using support cluster machines
(1) ddc 510
(1) expression
(1) gaussian process
(1) hyperacuity
(1) in proc 14th annu
(1) judgment
(1) learning
(1) memory consolidation
(1) neurons
(1) nystroem approximation
(1) pattern recognition
(1) responses
(1) reward
(1) signal
(1) sonderforschungsbereich 386
(1) speech
(1) stimuli
(1) systems
(1) visual cortex
Subject:
Dewey Decimal Classification (DDC)
(4) Computer science, knowledge & systems [00*]
(1) Mathematics [51*]
(1) Physics [53*]
(1) Medicine & health [61*]
Dewey Decimal Classification (DDC):
Year of Publication
(13) 2001
(5) 2009
(2) 2000
(2) 2013
(1) 2007
(1) 2008
(1) 2010
(1) 2011
Year of Publication:
Content Provider
(12) London Univ. College: UCL Discovery
(10) CiteSeerX
(2) Lausanne Ecole Polytechnique Fed.: Infoscience
(1) Munich LMU: Open Access
(1) Bath Univ.: OPus
Content Provider:
Language
(14) Unknown
(12) English
Language:
Document Type
(12) Text
(12) Reports, Papers, Lectures
(1) Article, Journals
(1) Books
Document Type:
Access
(15) Unknown
(11) Open Access
Access:
More Options
»
Search History
»
Get RSS Feed
»
Get ATOM Feed
»
Email this Search
»
Save Search
»
Browsing
»
Search Plugin
Further result pages
Results:
1

2

3
Next »
New Search »
Currently in BASE: 72,045,933 Documents of 3,464
Content Sources
About BASE

Contact

BASE Lab

Imprint
© 20042015 by
Bielefeld University Library
Search powered by
Solr
&
VuFind
.
Suggest Repository
BASE Interfaces
Currently in BASE: 72,045,933 Documents of 3,464 Content Sources
http://www.basesearch.net