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AI 2002 Australian Conference on Artificial Intelligence

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AI
2002
Springer

194views Artificial Intelligence» more AI 2002»

Ensembling neural networks: Many could be better than all

14 years 9 days ago

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Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the ensemble and its compone...

Zhi-Hua Zhou, Jianxin Wu, Wei Tang

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Id	Views	Title	Status

1	194	Ensembling neural networks: Many could be better than all	claim
2	171	Multiagent learning using a variable learning rate	claim
3	166	Algorithm for optimal winner determination in combinatorial auctions	claim
4	162	Learning Bayesian networks from data: An information-theory based approach	claim
5	161	Explanations, belief revision and defeasible reasoning	claim
6	159	Conditional independence in propositional logic	claim
7	154	Parallel Randomized Best-First Minimax Search	claim
8	151	Mining Incremental Association Rules with Generalized FP-Tree	claim
9	145	Coherence in finite argument systems	claim
10	144	A Constructive Approach to Parsing with Neural Networks - The Hybrid Connectionist Parsing Method	claim
11	139	QUANTUM: A Function-Based Question Answering System	claim
12	134	The influence of social norms and social consciousness on intention reconciliation	claim
13	133	Improving heuristic mini-max search by supervised learning	claim
14	129	On the computational complexity of assumption-based argumentation for default reasoning	claim
15	125	The size distribution for Markov equivalence classes of acyclic digraph models	claim
16	122	Modeling Organizational Rules in the Multi-agent Systems Engineering Methodology	claim
17	117	Programming backgammon using self-teaching neural nets	claim
18	108	Learning cost-sensitive active classifiers	claim
19	107	Using similarity criteria to make issue trade-offs in automated negotiations	claim
20	107	Combining topological and size information for spatial reasoning	claim
21	107	Disjoint pattern database heuristics	claim
22	102	On the relationship between model-based debugging and program slicing	claim
23	101	A re-weighting strategy for improving margins	claim
24	101	Binary vs. non-binary constraints	claim
25	99	Nagging: A scalable fault-tolerant paradigm for distributed search	claim
26	98	Emergence of social conventions in complex networks	claim
27	97	A hierarchical approach to computer Hex	claim
28	97	Games solved: Now and in the future	claim
29	96	On the Role of Contextual Weak Independence in Probabilistic Inference	claim
30	90	Multi-agent oriented constraint satisfaction	claim
31	89	Using Inter-agent Trust Relationships for Efficient Coalition Formation	claim
32	89	Recycling the Cycle of Perception: A Polemic	claim
33	79	Relaxed Unification - Proposal	claim
34	74	An information-based neural approach to generic constraint satisfaction	claim
35	71	Skepticism and floating conclusions	claim

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