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» Dead-End Driven Learning
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110
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BMCBI
2010
133views more  BMCBI 2010»
15 years 21 days ago
Improving de novo sequence assembly using machine learning and comparative genomics for overlap correction
Background: With the rapid expansion of DNA sequencing databases, it is now feasible to identify relevant information from prior sequencing projects and completed genomes and appl...
Lance E. Palmer, Mathäus Dejori, Randall A. B...
106
Voted
AI
2002
Springer
15 years 14 days ago
Improving heuristic mini-max search by supervised learning
This article surveys three techniques for enhancing heuristic game-tree search pioneered in the author's Othello program Logistello, which dominated the computer Othello scen...
Michael Buro
96
Voted
ELPUB
1999
ACM
15 years 4 months ago
Learning Curves: Managing Smooth Product Development Cycles in Non-Print Environments
and abstract entry. But since the burgeoning of the scholarly literature since World War II, these processes had become well-known and expertly done by most organizations in the pu...
Jill O'Neill, Chris Leonard
GECCO
2005
Springer
141views Optimization» more  GECCO 2005»
15 years 6 months ago
Constructing good learners using evolved pattern generators
Self-organization of brain areas in animals begins prenatally, evidently driven by spontaneously generated internal patterns. The neural structures continue to develop postnatally...
Vinod K. Valsalam, James A. Bednar, Risto Miikkula...
ICCV
2009
IEEE
16 years 5 months ago
Semi-Supervised Random Forests
Random Forests (RFs) have become commonplace in many computer vision applications. Their popularity is mainly driven by their high computational efficiency during both training ...
Christian Leistner, Amir Saffari, Jakob Santner, H...