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» Margin based Active Learning for LVQ Networks
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CORR
2010
Springer
163views Education» more  CORR 2010»
14 years 7 months ago
Faster Rates for training Max-Margin Markov Networks
Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (M3 N) is an effective approach. All state-of-...
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
ICONIP
2008
14 years 11 months ago
A Novel Approach for Hardware Based Sound Classification
Several applications would emerge from the development of efficient and robust sound classification systems able to identify the nature of non-speech sound sources. This paper prop...
Mauricio Kugler, Victor Alberto Parcianello Benso,...
FGCN
2008
IEEE
155views Communications» more  FGCN 2008»
14 years 11 months ago
Modeling the Marginal Distribution of Gene Expression with Mixture Models
We report the results of fitting mixture models to the distribution of expression values for individual genes over a broad range of normal tissues, which we call the marginal expr...
Edward Wijaya, Hajime Harada, Paul Horton
ESANN
2007
14 years 11 months ago
A supervised learning approach based on STDP and polychronization in spiking neuron networks
We propose a network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity), a temporal Hebbian unsupervised learning mode, biol...
Hélène Paugam-Moisy, Régis Ma...
NIPS
2003
14 years 11 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller