Refine
H-BRS Bibliography
- no (14) (remove)
Departments, institutes and facilities
Document Type
- Conference Object (6)
- Article (5)
- Report (2)
- Part of a Book (1)
Year of publication
Has Fulltext
- no (14)
Keywords
- Benutzeroberfläche (2)
- maschinelles Lernen (2)
- (Self-) perception (1)
- Affective interaction (1)
- Concurrent Kleene Algebra (1)
- Context awareness (1)
- Laws of programming (1)
- Philosophy of mind (1)
- Refinement (1)
- Semantic models (1)
A trace of the execution of a concurrent object-oriented program can be displayed in two-dimensions as a diagram of a non-metric finite geometry. The actions of a programs are represented by points, its objects and threads by vertical lines, its transactions by horizontal lines, its communications and resource sharing by sloping arrows, and its partial traces by rectangular figures.
The problem of filtering relevant information from the huge amount of available data is tackled by using models of the user's interest in order to discriminate interesting information from un-interesting data. As a consequence, Machine Learning for User Modeling (ML4UM) has become a key technique in recent adaptive systems. This article presents the novel approach of conceptual user models which are easy to understand and which allow for the system to explain its actions to the user. We show that ILP can be applied for the task of inducing user models from even sparse feedback by mutual sample enlargement. Results are evaluated independently of domain knowledge within a clear machine learning problem definition. The whole concept presented is realized in a meta web search engine, OySTER.
Machine learning seems to offer the solution to many problems in user modelling. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions to machine learning. This article closely relates the user modelling problem to the machine learning problem. It explicates some inherent dilemmas that are likely to be overlooked when applying machine learning algorithms in user modelling. Some examples illustrate how specific approaches deliver satisfying results and discuss underlying assumptions on the domain or how learned hypotheses relate to the requirements on the user model. Finally, some new or underestimated approaches offering promising perspectives in combined systems are discussed. The article concludes with a tentative "checklist" that one might like to consider when planning to apply machine learning to user modelling techniques.
The World Wide Web (Www) offers a huge number of documents which deal with information concerning nearly any topic. Thus, search engines and meta search engines currently are the key to finding information. Search engines with crawler based indexes vary in recall and offer a very bad precision. Meta search engines try to overcome these lacks by simple methods for information extraction, information filtering and integration of heterogenous information resources. Only few search engines employ intelligent techniques in order to increase precision.
Machine Learning seems to offer the solution to the central problem in recommender systems: Learning to recommend interesting items from observations. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions from Machine Learning. This article relates the problem of recommendation by user modeling closely to the machine learning problem and explicates some inherent dilemmas. A few examples will illustrate specific approaches and discuss underlying assumptions on the domain or how learned hypotheses relate to requirements on the user model. The article concludes with a tentative 'checklist' that one might like to consider when thinking about to use Machine Learning in User Adaptive environments such as recommender systems.
Roughness by Residuals
(2015)
Rough set theory (RST) focuses on forming posets of equivalence relations to describe sets with increasing accuracy. The connection between modal logics and RST is well known and has been extensively studied in their relation algebraic (RA) formalisation. RST has also been interpreted as a variant of intuitionistic or multi-valued logics and has even been studied in the context of logic programming.
On nothing
(2014)
Learning Adaptive Behavior
(2005)
The @neurIST project
(2008)