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ICML
1996
IEEE
14 years 9 months ago
Discretizing Continuous Attributes While Learning Bayesian Networks
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
Moisés Goldszmidt, Nir Friedman
VL
2010
IEEE
216views Visual Languages» more  VL 2010»
13 years 7 months ago
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...
CGF
2005
252views more  CGF 2005»
13 years 9 months ago
Support Vector Machines for 3D Shape Processing
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which...
Florian Steinke, Bernhard Schölkopf, Volker B...
ACTAC
2006
126views more  ACTAC 2006»
13 years 9 months ago
Named Entity Recognition for Hungarian Using Various Machine Learning Algorithms
In this paper we introduce a statistical Named Entity recognizer (NER) system for the Hungarian language. We examined three methods for identifying and disambiguating proper nouns...
Richárd Farkas, György Szarvas, Andr&a...
ECTEL
2007
Springer
14 years 3 months ago
Profiled Search Methods for e-Learning Systems
Most currently used e-Learning Systems do not often offer search functionality. Even if methods are provided to search for Learning Objects (LOs), they don’t usually utilize info...
Tomasz Orzechowski, Sebastian Ernst, Andrzej Dziec...