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» The Inefficiency of Batch Training for Large Training Sets
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JMLR
2007
104views more  JMLR 2007»
13 years 6 months ago
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"
In a recently published paper in JMLR, Tsang et al. (2005) present an algorithm for SVM called Core Vector Machines (CVM) and illustrate its performances through comparisons with ...
Gaëlle Loosli, Stéphane Canu
DATAMINE
1999
140views more  DATAMINE 1999»
13 years 6 months ago
A Scalable Parallel Algorithm for Self-Organizing Maps with Applications to Sparse Data Mining Problems
Abstract. We describe a scalable parallel implementation of the self organizing map (SOM) suitable for datamining applications involving clustering or segmentation against large da...
Richard D. Lawrence, George S. Almasi, Holly E. Ru...
ESANN
2006
13 years 7 months ago
An algorithm for fast and reliable ESOM learning
The training of Emergent Self-organizing Maps (ESOM ) with large datasets can be a computationally demanding task. Batch learning may be used to speed up training. It is demonstrat...
Mario Nöcker, Fabian Mörchen, Alfred Ult...
ICMLA
2009
13 years 4 months ago
The Neuro Slot Car Racer: Reinforcement Learning in a Real World Setting
This paper describes a novel real-world reinforcement learning application: The Neuro Slot Car Racer. In addition to presenting the system and first results based on Neural Fitted...
Tim C. Kietzmann, Martin Riedmiller
NIPS
2003
13 years 7 months ago
Large Scale Online Learning
We consider situations where training data is abundant and computing resources are comparatively scarce. We argue that suitably designed online learning algorithms asymptotically ...
Léon Bottou, Yann LeCun