PKDD 2009 - PDF Preprint Papers

PKDD 2009 European Symposium/Conference on Principles of Data Mining and Knowledge Discovery

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PKDD
2009
Springer

269views Data Mining» more PKDD 2009»

Enhanced Web Page Content Visualization with Firefox

14 years 7 months ago

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This paper aims at presenting how natural language processing and machine learning techniques can help the internet surfer to get a better overview of the pages he is reading. The ...

Lorand Dali, Delia Rusu, Dunja Mladenic

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Id	Views	Title	Status

1	269	Enhanced Web Page Content Visualization with Firefox	claim
2	196	Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective	claim
3	184	Learning Preferences with Hidden Common Cause Relations	claim
4	184	Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm	claim
5	181	Active Learning for Reward Estimation in Inverse Reinforcement Learning	claim
6	175	Latent Dirichlet Bayesian Co-Clustering	claim
7	174	Active and Semi-supervised Data Domain Description	claim
8	170	Statistical Relational Learning with Formal Ontologies	claim
9	169	Hybrid Least-Squares Algorithms for Approximate Policy Evaluation	claim
10	169	Combining Instance-Based Learning and Logistic Regression for Multilabel Classification	claim
11	164	Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification	claim
12	162	A Convex Method for Locating Regions of Interest with Multi-instance Learning	claim
13	155	Dynamic Factor Graphs for Time Series Modeling	claim
14	153	Subspace Regularization: A New Semi-supervised Learning Method	claim
15	152	Feature Selection for Value Function Approximation Using Bayesian Model Selection	claim
16	150	Omiotis: A Thesaurus-Based Measure of Text Relatedness	claim
17	149	Learning to Disambiguate Search Queries from Short Sessions	claim
18	148	Feature Selection by Transfer Learning with Linear Regularized Models	claim
19	147	Kernel Polytope Faces Pursuit	claim
20	146	Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks	claim
21	144	Compositional Models for Reinforcement Learning	claim
22	144	Evaluation Measures for Multi-class Subgroup Discovery	claim
23	144	Heteroscedastic Probabilistic Linear Discriminant Analysis with Semi-supervised Extension	claim
24	141	Binary Decomposition Methods for Multipartite Ranking	claim
25	138	Margin and Radius Based Multiple Kernel Learning	claim
26	138	Cost-Sensitive Learning Based on Bregman Divergences	claim
27	137	On Subgroup Discovery in Numerical Domains	claim
28	136	Integrating Logical Reasoning and Probabilistic Chain Graphs	claim
29	135	Syntactic Structural Kernels for Natural Language Interfaces to Databases	claim
30	134	Mining Graph Evolution Rules	claim
31	134	Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)	claim
32	132	Decomposition Algorithms for Training Large-Scale Semiparametric Support Vector Machines	claim
33	129	RTG: A Recursive Realistic Graph Generator Using Random Typing	claim
34	129	Considering Unseen States as Impossible in Factored Reinforcement Learning	claim
35	124	Capacity Control for Partially Ordered Feature Sets	claim
36	120	Variational Graph Embedding for Globally and Locally Consistent Feature Extraction	claim
37	118	Sparse Kernel SVMs via Cutting-Plane Training	claim
38	118	The Feature Importance Ranking Measure	claim
39	118	Protein Identification from Tandem Mass Spectra with Probabilistic Language Modeling	claim
40	117	New Regularized Algorithms for Transductive Learning	claim
41	116	On Feature Selection, Bias-Variance, and Bagging	claim
42	114	A Matrix Factorization Approach for Integrating Multiple Data Views	claim
43	113	Feature Selection for Density Level-Sets	claim
44	112	Max-Margin Weight Learning for Markov Logic Networks	claim
45	111	Found in Translation	claim
46	110	Reconstructing Data Perturbed by Random Projections When the Mixing Matrix Is Known	claim
47	110	Using Temporal Language Models for Document Dating	claim
48	109	Learning Multi-linear Representations of Distributions for Efficient Inference	claim
49	108	Taxonomy-Driven Lumping for Sequence Mining	claim
50	108	Latent Dirichlet Allocation for Automatic Document Categorization	claim
51	107	Neural Networks for State Evaluation in General Game Playing	claim
52	104	Leveraging Higher Order Dependencies between Features for Text Classification	claim
53	103	Kernels for Periodic Time Series Arising in Astronomy	claim
54	102	A Generalization of Forward-Backward Algorithm	claim
55	102	Relevance Grounding for Planning in Relational Domains	claim
56	102	Are We There Yet?	claim
57	95	Non-redundant Subgroup Discovery Using a Closure System	claim
58	94	OTTHO: On the Tip of My THOught	claim
59	92	A Generic Approach to Topic Models	claim
60	88	Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs	claim

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