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1.
Robust MultiTask Learning with tProcesses
Open Access
Title:
Robust MultiTask Learning with tProcesses
Author:
Shipeng Yu
;
Kai Yu
Shipeng Yu
;
Kai Yu
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Description:
Most current multitask learning frameworks ignore the robustness issue, which means that the presence of “outlier ” tasks may greatly reduce overall system performance. We introduce a robust framework for Bayesian multitask learning, tprocesses (TP), which are a generalization of Gaussian processes (GP) for multitask learning. TP allows the s...
Most current multitask learning frameworks ignore the robustness issue, which means that the presence of “outlier ” tasks may greatly reduce overall system performance. We introduce a robust framework for Bayesian multitask learning, tprocesses (TP), which are a generalization of Gaussian processes (GP) for multitask learning. TP allows the system to effectively distinguish good tasks from noisy or outlier tasks. Experiments show that TP not only improves overall system performance, but can also serve as an indicator for the “informativeness ” of different tasks. 1.
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Year of Publication:
20080701
Source:
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/icml2007_tp.pdf
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/icml2007_tp.pdf
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en
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.69.1729
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/icml2007_tp.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.69.1729
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/icml2007_tp.pdf
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2.
Blockwise supervised inference on large graphs
Open Access
Title:
Blockwise supervised inference on large graphs
Author:
Kai Yu
;
Shipeng Yu
Kai Yu
;
Shipeng Yu
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Description:
In this paper we consider supervised learning on largescale graphs, which is highly demanding in terms of time and memory costs. We demonstrate that, if a graph has a bipartite structure that contains a small set of nodes separating the remaining from each other, the inference can be equivalently done over an induced graph connecting only the s...
In this paper we consider supervised learning on largescale graphs, which is highly demanding in terms of time and memory costs. We demonstrate that, if a graph has a bipartite structure that contains a small set of nodes separating the remaining from each other, the inference can be equivalently done over an induced graph connecting only the separators. Since each separator influences a certain neighborhood, the method essentially explores the block structure of graphs to improve the scalability. In the next step, instead of identifying the bipartite structure in a given graph, which is often difficult, we propose to construct a set of separators via two methods, one is adjacency matrix factorization and the other is mixture models, which both naturally ends up with a bipartite graph and meanwhile preserves the original data structure. Finally we report results of experiments on a toy problem and an intrusion detection problem. 1.
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Year of Publication:
20080717
Source:
http://www.dbs.informatik.unimuenchen.de/~yu_k/block_graph_inference.pdf
http://www.dbs.informatik.unimuenchen.de/~yu_k/block_graph_inference.pdf
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en
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.1933
http://www.dbs.informatik.unimuenchen.de/~yu_k/block_graph_inference.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.1933
http://www.dbs.informatik.unimuenchen.de/~yu_k/block_graph_inference.pdf
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3.
Blockwise supervised inference on large graphs
Open Access
Title:
Blockwise supervised inference on large graphs
Author:
Kai Yu
;
Shipeng Yu
Kai Yu
;
Shipeng Yu
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Description:
In this paper we consider supervised learning on largescale graphs, which is highly demanding in terms of time and memory costs. We demonstrate that, if a graph has a bipartite structure that contains a small set of nodes separating the remaining from each other, the inference can be equivalently done over an induced graph connecting only the s...
In this paper we consider supervised learning on largescale graphs, which is highly demanding in terms of time and memory costs. We demonstrate that, if a graph has a bipartite structure that contains a small set of nodes separating the remaining from each other, the inference can be equivalently done over an induced graph connecting only the separators. Since each separator influences a certain neighborhood, the method essentially explores the block structure of graphs to improve the scalability. In the next step, instead of identifying the bipartite structure in a given graph, which is often difficult, we propose to construct a set of separators via two methods, one is adjacency matrix factorization and the other is mixture models, which both naturally ends up with a bipartite graph and meanwhile preserves the original data structure. Finally we report results of experiments on a toy problem and an intrusion detection problem. 1.
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Year of Publication:
20080717
Source:
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/block_graph_inference.pdf
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/block_graph_inference.pdf
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en
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.3874
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/block_graph_inference.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.3874
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/block_graph_inference.pdf
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4.
Knowledge
Open Access
Title:
Knowledge
Author:
Zhao Xu
;
Shipeng Yu
Zhao Xu
;
Shipeng Yu
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Description:
Social networks usually involve rich collections of objects, which are jointly linked into complex relational networks. Social network analysis has gained in importance due to the growing availability of data on novel social networks, e.g. citation networks, Web 2.0 social networks like facebook, and the hyperlinked internet. Recently, the infin...
Social networks usually involve rich collections of objects, which are jointly linked into complex relational networks. Social network analysis has gained in importance due to the growing availability of data on novel social networks, e.g. citation networks, Web 2.0 social networks like facebook, and the hyperlinked internet. Recently, the infinite hidden relational model (IHRM) has been developed for the analysis of complex relational domains. The IHRM extends the expressiveness of a relational model by introducing for each object an infinitedimensional hidden variable as part of a Dirichlet process mixture model. In this paper we discuss how the IHRM can be used to model and analyze social networks. In such an IHRMbased social network model, each edge is associated with a random variable (RV) and the probabilistic dependencies between these RVs are specified by the model
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Year of Publication:
20090407
Source:
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/snakdd08srl.pdf
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/snakdd08srl.pdf
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en
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.4132
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/snakdd08srl.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.4132
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/snakdd08srl.pdf
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5.
1 VIPS: a Visionbased Page Segmentation Algorithm
Open Access
Title:
1 VIPS: a Visionbased Page Segmentation Algorithm
Author:
Deng Cai
;
Shipeng Yu
;
Jirong Wen
;
Weiying Ma
;
Deng Cai
;
Shipeng Yu
;
Jirong Wen
;
Weiying Ma
Deng Cai
;
Shipeng Yu
;
Jirong Wen
;
Weiying Ma
;
Deng Cai
;
Shipeng Yu
;
Jirong Wen
;
Weiying Ma
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Description:
A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this structure. This paper presents an automatic topdown, tagtree independent approach to detect web content structure. It simu...
A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this structure. This paper presents an automatic topdown, tagtree independent approach to detect web content structure. It simulates how a user understands web layout structure based on his visual perception. Comparing to other existing techniques, our approach is independent to underlying documentation representation such as HTML and works well even when the HTML structure is far different from layout structure. Experiments show satisfactory results. 1
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Year of Publication:
20080815
Source:
http://research.microsoft.com/~jrwen/jrwen_files/publications/vips_technical report.pdf
http://research.microsoft.com/~jrwen/jrwen_files/publications/vips_technical report.pdf
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Document Type:
text
Language:
en
Subjects:
VIPS ; a Visionbased Page Segmentation Algorithm
VIPS ; a Visionbased Page Segmentation Algorithm
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.638
http://research.microsoft.com/~jrwen/jrwen_files/publications/vips_technical report.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.638
http://research.microsoft.com/~jrwen/jrwen_files/publications/vips_technical report.pdf
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6.
Local Learning Projections
Open Access
Title:
Local Learning Projections
Author:
Mingrui Wu
;
Kai Yu
;
Shipeng Yu
;
Bernhard Schölkopf
Mingrui Wu
;
Kai Yu
;
Shipeng Yu
;
Bernhard Schölkopf
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Description:
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essentially seeks the projection that has the minimal global estimation error. Then we propose a dimensionality reduction algorithm that leads to the projection with the min...
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essentially seeks the projection that has the minimal global estimation error. Then we propose a dimensionality reduction algorithm that leads to the projection with the minimal local estimation error, and elucidate its advantages for classification tasks. We also indicate that LLP keeps the local information in the sense that the projection value of each point can be well estimated based on its neighbors and their projection values. Experimental results are provided to validate the effectiveness of the proposed algorithm. 1.
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Year of Publication:
20081230
Source:
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/icml2007_llp.pdf
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/icml2007_llp.pdf
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Language:
en
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.127.9325
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/icml2007_llp.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.127.9325
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/icml2007_llp.pdf
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7.
Variational bayesian dirichletmultinomial allocation for mixture of exponential family distributions. manuscript
Open Access
Title:
Variational bayesian dirichletmultinomial allocation for mixture of exponential family distributions. manuscript
Author:
Shipeng Yu
;
Kai Yu
;
Volker Tresp
;
Hanspeter Kriegel
Shipeng Yu
;
Kai Yu
;
Volker Tresp
;
Hanspeter Kriegel
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Abstract. This paper studies a Bayesian framework for density modeling with mixture of exponential family distributions. Variational Bayesian DirichletMultinomial allocation (VBDMA) is introduced, which performs inference and learning efficiently using variational Bayesian methods and performs automatic model selection. The model is closely rel...
Abstract. This paper studies a Bayesian framework for density modeling with mixture of exponential family distributions. Variational Bayesian DirichletMultinomial allocation (VBDMA) is introduced, which performs inference and learning efficiently using variational Bayesian methods and performs automatic model selection. The model is closely related to Dirichlet process mixture models and demonstrates similar automatic model selection in the variational Bayesian context. 1
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Year of Publication:
20080717
Source:
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/ecml06vbdma.pdf
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/ecml06vbdma.pdf
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en
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.6934
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/ecml06vbdma.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.6934
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/ecml06vbdma.pdf
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8.
MultiOutput Regularized Projection
Open Access
Title:
MultiOutput Regularized Projection
Author:
Kai Yu
;
Shipeng Yu
;
Volker Tresp
Kai Yu
;
Shipeng Yu
;
Volker Tresp
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Description:
Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is of particular importance in predicting multivariate or structured outputs which is an area ...
Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is of particular importance in predicting multivariate or structured outputs which is an area of growing interest. In this paper we introduce a novel projection framework which is sensitive to both input features and outputs. Based on the derived features prediction accuracy can be greatly improved. We validate our approach in two applications. The first is to model users ’ preferences on a set of paintings. The second application is concerned with image categorization where each image may belong to multiple categories. The proposed algorithm produces very encouraging results in both settings. 1
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Year of Publication:
20090202
Source:
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/cvpr2005final.pdf
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/cvpr2005final.pdf
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en
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.9071
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/cvpr2005final.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.9071
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/cvpr2005final.pdf
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9.
Dirichlet Enhanced Relational Learning
Open Access
Title:
Dirichlet Enhanced Relational Learning
Author:
Zhao Xu
;
Kai Yu
;
Shipeng Yu
;
Hanspeter Kriegel
Zhao Xu
;
Kai Yu
;
Shipeng Yu
;
Hanspeter Kriegel
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Description:
We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be “personalized”, i.e., owned by entities or relationships, and are coupled via a common prior distribution. Flexibility is added in a nonparametric hierarchical Bayesian approach, such that the learned knowle...
We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be “personalized”, i.e., owned by entities or relationships, and are coupled via a common prior distribution. Flexibility is added in a nonparametric hierarchical Bayesian approach, such that the learned knowledge can be truthfully represented. We apply our approach to a medical domain where we form a nonparametric hierarchical Bayesian model for relations involving hospitals, patients, procedures and diagnosis. The experiments show that the additional flexibility in a nonparametric hierarchical Bayes approach results in a more accurate model of the dependencies between procedures and diagnosis and gives significantly improved estimates of the probabilities of future procedures. 1.
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Year of Publication:
20080701
Source:
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/icml05_315.pdf
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/icml05_315.pdf
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.3413
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/icml05_315.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.3413
http://wwwbrauer.informatik.tumuenchen.de/~trespvol/papers/icml05_315.pdf
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10.
Search and Retrieval—information filtering, retrieval models
Open Access
Title:
Search and Retrieval—information filtering, retrieval models
Author:
Kai Yu
;
Volker Tresp
;
Shipeng Yu
Kai Yu
;
Volker Tresp
;
Shipeng Yu
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Description:
Information filtering has made considerable progress in recent years.The predominant approaches are contentbased methods and collaborative methods. Researchers have largely concentrated on either of the two approaches since a principled unifying framework is still lacking. This paper suggests that both approaches can be combined under a hierarc...
Information filtering has made considerable progress in recent years.The predominant approaches are contentbased methods and collaborative methods. Researchers have largely concentrated on either of the two approaches since a principled unifying framework is still lacking. This paper suggests that both approaches can be combined under a hierarchical Bayesian framework. Individual contentbased user profiles are generated and collaboration between various user models is achieved via a common learned prior distribution. However, it turns out that a parametric distribution (e.g. Gaussian) is too restrictive to describe such a common learned prior distribution. We thus introduce a nonparametric common prior, which is a sample generated from a Dirichlet process which assumes the role of a hyper prior. We describe effective means to learn this nonparametric distribution, and apply it to learn users ’ information needs. The resultant algorithm is simple and understandable, and offers a principled solution to combine contentbased filtering and collaborative filtering. Within our framework, we are now able to interpret various existing techniques from a unifying point of view. Finally we demonstrate the empirical success of the proposed information filtering methods.
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Year of Publication:
20081230
Source:
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/SIGIR04_p276yu.pdf
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/SIGIR04_p276yu.pdf
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Subjects:
Algorithms ; Theory ; Human Factors Keywords Collaborative Filtering ; ContentBased Filtering ; Dirichlet Process ; Nonparametric Bayesian Modelling
Algorithms ; Theory ; Human Factors Keywords Collaborative Filtering ; ContentBased Filtering ; Dirichlet Process ; Nonparametric Bayesian Modelling
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.3664
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/SIGIR04_p276yu.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.3664
http://www.dbs.informatik.unimuenchen.de/~spyu/paper/SIGIR04_p276yu.pdf
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(3) Luca Bogoni
(3) Matthias Schubert
(3) Rehman, Shahnaz
(3) Rómer Rosales
(3) Shuiwang Ji
(3) Sriram Krishnan
(3) Vacek, Thomas P
(3) Yang Yang
(3) Yi Huang
(3) Zheng Zhao
(2) Anna Jerebko
(2) Arun Krishnan
(2) Bernhard Schölkopf
(2) Bin B. Zhu
(2) Cary Dehingoberije
(2) Charles Florin
(2) Faisal Farooq
(2) Farooq, Faisal
(2) Gerardo Hermosillo Valadez
(2) Jinming Li
(2) Ketelaar, Tijs
(2) Krishnapuram, Balaji
(2) Li, Bo
(2) Libo Yang
(2) Linda H. Zhao
(2) Linda Moy
(2) Liu, ChunMing
(2) Marcos Salganicoff
(2) Markus Bundschus
(2) Mathaeus Dejori
(2) Meizhu Liu
(2) Murat Dundar
(2) Ren, Shichao
(2) Shipeng Sun
(2) Tielin He
(2) Wei Chu
(2) Wei, Shipeng
(2) Wenli Li
(2) Xiaojing Ye
(2) Yu Sun
(2) Yu, Dali
(2) Yunfeng Lin
(1) Achim Rettinger
(1) Assaly, Ragheb
(1) Bayesian Cotraining
(1) Bernhard Schlkopf
(1) C. Raykar
(1) Carsten Rother
(1) Chen, Min
(1) Chen, Qiang
(1) David Blei
(1) De Ruysscher, Dirk; U0019241; JFA; CORA;...
(1) DehingOberije, Cary; JFA; CORA; De Ruysscher,...
(1) DehingOberije, Cary; JFA; CORA; Yu, Shipeng;...
(1) Dissertation Im Fach Informatik
(1) Dix, Thomas A.
(1) Dong, Fang
(1) Dong, Yanhong
(1) Dr. Volker Tresp
(1) Edward Rasmussen
(1) Eltahawy, Ehab A
(1) Emons, Anne Mie C.
(1) Emons, AnneMie C.
(1) Estell, Kim
(1) Fraunhofer Iais
(1) Fung, Glenn
(1) Gang Zeng
Author:
Subject
(9) theory
(6) algorithms
(5) content analysis and indexing indexing methods...
(5) measurement
(5) performance
(5) supervised projection
(4) research article
(3) articles
(3) categories and subject descriptors h 3...
(3) lcc q
(3) lcc science
(2) case report
(2) content based filtering
(2) design
(2) dimensionality reduction
(2) dirichlet process
(2) doaj computer science
(2) doaj technology and engineering
(2) experimentation
(2) general terms algorithms
(2) h 3 information storage and retrieval
(2) human factors keywords collaborative filtering
(2) human factors keywords web information retrieval
(2) i 2 artificial intelligence
(2) injury
(2) lcc electronic computers computer science
(2) lcc instruments and machines
(2) lcc mathematics
(2) lcc qa1 939
(2) lcc qa71 90
(2) lcc qa75 5 76 95
(2) miscellaneous general terms algorithms
(2) multi label classification
(2) nonparametric bayesian modelling
(2) page segmentation
(2) passage retrieval
(2) performance keywords dimensionality reduction
(2) performance keywords privacy preserving data...
(2) principal component analysis
(2) radiotherapy
(2) relevance
(2) semi supervised
(1) 1980
(1) 1999
(1) 2000
(1) 3 dimensional conformal radiotherapy
(1) a vision based page segmentation algorithm
(1) and gene regulation
(1) approximate nearest neighbor search
(1) article
(1) as an inter disciplinary area
(1) bi level video
(1) binary code ranking
(1) cancer patients
(1) co training
(1) cohen et al
(1) computer science computer vision and pattern...
(1) concurrent chemoradiation
(1) cost sensitive learning
(1) cox re
(1) cox regression
(1) crowdsourcing 1 supervised learning from...
(1) doaj biology
(1) doaj biology and life sciences
(1) doaj health sciences
(1) doaj medicine general
(1) doaj microbiology
(1) dose escalation
(1) dosimetric parameters
(1) dyspnea
(1) escalation
(1) experimentation keywords computer aided diagnosis
(1) fakultät für mathematik
(1) fibrosis
(1) gaussian processes
(1) herbrich et al
(1) human factors keywords page segmentation
(1) hyperfractionated accelerated radiotherapy
(1) induced pneumonitis
(1) informatik und statistik
(1) keywords
(1) km
(1) lcc medicine
(1) lcc medicine general
(1) lcc microbiology
(1) lcc qr1 502
(1) lcc r
(1) lcc r5 920
(1) learning general terms algorithms
(1) lookup table
(1) lung
(1) lung cancer
(1) matrix factorization
(1) mobile video conferencing
(1) molecular biology
(1) multi view learning
(1) multiple annotators
(1) multiple experts
(1) multiple teachers
(1) neoadjuvant chemotherapy
Subject:
Dewey Decimal Classification (DDC)
(33) Computer science, knowledge & systems [00*]
(8) Medicine & health [61*]
(4) Statistics [31*]
(2) Science [50*]
(2) Engineering [62*]
(1) Epistemology [12*]
(1) Social sciences, sociology & anthropology...
(1) Mathematics [51*]
(1) Plants (Botany) [58*]
(1) Arts [70*]
(1) History of Asia [95*]
Dewey Decimal Classification (DDC):
Year of Publication
(35) 2008
(22) 2009
(19) 2013
(13) 2010
(7) 2014
(6) 2011
(4) 2012
(3) 2015
(2) 2007
(1) 2006
Year of Publication:
Content Provider
(87) CiteSeerX
(14) PubMed Central
(3) DOAJ Articles
(3) Leuven KU: Lirias
(1) ArXiv.org
(1) BioMed Central
(1) Hindawi Publishing Corporation
(1) Munich LMU: Digital theses
(1) Queensland Univ.: UQ eSpace
Content Provider:
Language
(110) English
(2) Unknown
Language:
Document Type
(102) Text
(7) Article, Journals
(2) Reports, Papers, Lectures
(1) Theses
Document Type:
Access
(106) Open Access
(6) Unknown
Access:
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