Sciweavers

4930 search results - page 92 / 986
» Learning minimal abstractions
Sort
View
ML
2006
ACM
110views Machine Learning» more  ML 2006»
15 years 4 months ago
Classification-based objective functions
Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, ...
Michael Rimer, Tony Martinez
ICML
2008
IEEE
16 years 5 months ago
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
Nicolas Loeff, David A. Forsyth, Deepak Ramachandr...
CORR
2006
Springer
127views Education» more  CORR 2006»
15 years 4 months ago
Semi-Supervised Learning -- A Statistical Physics Approach
We present a novel approach to semisupervised learning which is based on statistical physics. Most of the former work in the field of semi-supervised learning classifies the point...
Gad Getz, Noam Shental, Eytan Domany
BMCBI
2008
88views more  BMCBI 2008»
15 years 4 months ago
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
Background: By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Na
Myron Peto, Andrzej Kloczkowski, Vasant Honavar, R...
NAACL
2003
15 years 5 months ago
Unsupervised Learning of Morphology for English and Inuktitut
We describe a simple unsupervised technique for learning morphology by identifying hubs in an automaton. For our purposes, a hub is a node in a graph with in-degree greater than o...
Howard Johnson, Joel D. Martin