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» Learning Bounds for Domain Adaptation
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119
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BIS
2006
132views Business» more  BIS 2006»
15 years 1 months ago
Utilizing Successful Work Practice for Business Process Evolution
Business process management (BPM) has emerged as a dominant technology in current enterprise systems and business solutions. However, business processes are always evolving in cur...
Ruopeng Lu, Shazia Wasim Sadiq, Guido Governatori
77
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GECCO
2009
Springer
110views Optimization» more  GECCO 2009»
15 years 5 months ago
EMO shines a light on the holes of complexity space
Typical domains used in machine learning analyses only partially cover the complexity space, remaining a large proportion of problem difficulties that are not tested. Since the ac...
Núria Macià, Albert Orriols-Puig, Es...
PKDD
2009
Springer
184views Data Mining» more  PKDD 2009»
15 years 5 months ago
Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm
Abstract. This paper focuses on Active Learning with a limited number of queries; in application domains such as Numerical Engineering, the size of the training set might be limite...
Philippe Rolet, Michèle Sebag, Olivier Teyt...
96
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EMNLP
2006
15 years 1 months ago
Unsupervised Discovery of a Statistical Verb Lexicon
This paper demonstrates how unsupervised techniques can be used to learn models of deep linguistic structure. Determining the semantic roles of a verb's dependents is an impo...
Trond Grenager, Christopher D. Manning
JUCS
2008
180views more  JUCS 2008»
15 years 14 days ago
The APS Framework For Incremental Learning of Software Agents
Abstract: Adaptive behavior and learning are required of software agents in many application domains. At the same time agents are often supposed to be resource-bounded systems, whi...
Damian Dudek