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ML 2008 Workshop on Machine Learrning

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2008
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Feature selection via sensitivity analysis of SVM probabilistic outputs

13 years 4 months ago

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Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) l...

Kai Quan Shen, Chong Jin Ong, Xiao Ping Li, Einar ...

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Id	Views	Title	Status

1	248	Feature selection via sensitivity analysis of SVM probabilistic outputs	claim
2	222	Boosted Bayesian network classifiers	claim
3	174	ALLPAD: approximate learning of logic programs with annotated disjunctions	claim
4	162	Incorporating prior knowledge in support vector regression	claim
5	156	On the connection between the phase transition of the covering test and the learning success rate in ILP	claim
6	152	Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path	claim
7	150	Learning probabilistic logic models from probabilistic examples	claim
8	146	Improving maximum margin matrix factorization	claim
9	135	Compiling pattern matching to good decision trees	claim
10	134	Multilabel classification via calibrated label ranking	claim
11	128	QG/GA: a stochastic search for Progol	claim
12	115	Calling variadic functions from a strongly-typed language	claim
13	110	A theory of learning with similarity functions	claim
14	109	O'browser: objective caml on browsers	claim
15	109	Caml-Shcaml: an ocaml library for unix shell programming	claim
16	104	Many holes in hindley-milner	claim
17	102	Inductive logic programming for gene regulation prediction	claim
18	101	On reoptimizing multi-class classifiers	claim
19	100	Generalized ordering-search for learning directed probabilistic logical models	claim
20	100	Compressing probabilistic Prolog programs	claim
21	100	Convex multi-task feature learning	claim
22	99	A bias/variance decomposition for models using collective inference	claim
23	98	Unrestricted pure call-by-value recursion	claim
24	96	Discovering significant patterns	claim
25	95	Transfer in variable-reward hierarchical reinforcement learning	claim
26	94	Incremental exemplar learning schemes for classification on embedded devices	claim
27	91	Large margin vs. large volume in transductive learning	claim
28	88	Inductive process modeling	claim
29	87	Lwt: a cooperative thread library	claim
30	87	Layered critical values: a powerful direct-adjustment approach to discovering significant patterns	claim
31	87	Margin-based first-order rule learning	claim
32	86	Structured machine learning: the next ten years	claim
33	80	A notion of task relatedness yielding provable multiple-task learning guarantees	claim
34	78	A linear fit gets the correct monotonicity directions	claim
35	74	Flexible latent variable models for multi-task learning	claim

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