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2,139
Learning in the Presence of Concept Drift and Hidden Contexts
- Machine Learning
, 1996
"... . On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and c ..."
Abstract
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Cited by 285 (1 self)
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. On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift
Extracting Hidden Context
, 1997
"... Concept drift due to hidden changes in context complicates learning in many domains including financial prediction, medical diagnosis, and network performance. Existing machine learning approaches to this problem use an incremental learning, on-line paradigm. Batch, offline learners tend to be i ..."
Abstract
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Cited by 50 (2 self)
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Concept drift due to hidden changes in context complicates learning in many domains including financial prediction, medical diagnosis, and network performance. Existing machine learning approaches to this problem use an incremental learning, on-line paradigm. Batch, offline learners tend
Recurring Hidden Contexts in Online Concept Learning
"... Abstract. Learning systems in dynamic environments have to be able to process examples that occurred in different hidden contexts. We review several approaches to hidden context aware concept learning systems and question the appropriateness of the Calendar Apprentice domain as a real world benchmar ..."
Abstract
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Cited by 2 (2 self)
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Abstract. Learning systems in dynamic environments have to be able to process examples that occurred in different hidden contexts. We review several approaches to hidden context aware concept learning systems and question the appropriateness of the Calendar Apprentice domain as a real world
Hidden Context Highlighting with JPEG2000-Imagery
"... This contribution motivates and proposes the new idea of embedding and hiding a highlighting of particular regions within a raster image. Existing approaches tightly combine content and means for highlighting, and thus, do not allow access control to the emphasized contents or a removal of the accen ..."
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. The achieved results show that hidden context highlighting is rather appropriate for emphasizing pre-defined image regions and able to control the access to the respective contents.
Learning about User in the Presence of Hidden Context
- Proceedings of the UM2001 Workshop on Machine Learning for User Modeling
, 2001
"... . This paper presents an algorithm for learning drifting and recurring ..."
Abstract
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Cited by 3 (0 self)
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. This paper presents an algorithm for learning drifting and recurring
Discovering Temporal Hidden Contexts in Web Sessions for User Trail Prediction
"... In many web information systems such as e-shops and information portals, predictive modeling is used to understand user’s intentions based on their browsing behaviour. User behavior is inherently sensitive to various hidden contexts. It has been shown in different experimental studies that exploitat ..."
Abstract
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Cited by 1 (1 self)
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In many web information systems such as e-shops and information portals, predictive modeling is used to understand user’s intentions based on their browsing behaviour. User behavior is inherently sensitive to various hidden contexts. It has been shown in different experimental studies
Exploiting Generative Models in Discriminative Classifiers
- In Advances in Neural Information Processing Systems 11
, 1998
"... Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often resu ..."
Abstract
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Cited by 551 (9 self)
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Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often
Toolglass and magic lenses: The see-through interface
, 1993
"... Toolglass ™ widgets are new user interface tools that can appear, as though on a transparent sheet of glass, between an application and a traditional cursor. They can be positioned with one hand while the other positions the cursor. The widgets provide a rich and concise vocabulary for operating on ..."
Abstract
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Cited by 506 (8 self)
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on application objects. These widgets may incorporate visual filters, called Magic Lens™ filters, that modify the presentation of application objects to reveal hidden information, to enhance data of interest, or to suppress distracting information. Together, these tools form a see-through interface that offers
Finding structure in time
- COGNITIVE SCIENCE
, 1990
"... Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a pro ..."
Abstract
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Cited by 2071 (23 self)
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proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context
Learning Stochastic Logic Programs
, 2000
"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic context-free grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a first-order r ..."
Abstract
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Cited by 1194 (81 self)
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Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic context-free grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a first
Results 1 - 10
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