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» A New Approximate Maximal Margin Classification Algorithm
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COLT
2008
Springer
13 years 9 months ago
On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms
Boosting algorithms build highly accurate prediction mechanisms from a collection of lowaccuracy predictors. To do so, they employ the notion of weak-learnability. The starting po...
Shai Shalev-Shwartz, Yoram Singer
ICASSP
2010
IEEE
13 years 7 months ago
Large margin estimation of n-gram language models for speech recognition via linear programming
We present a novel discriminative training algorithm for n-gram language models for use in large vocabulary continuous speech recognition. The algorithm uses large margin estimati...
Vladimir Magdin, Hui Jiang
ICCV
2009
IEEE
15 years 14 days ago
Max-Margin Additive Classifiers for Detection
We present methods for training high quality object detectors very quickly. The core contribution is a pair of fast training algorithms for piece-wise linear classifiers, which ...
Subhransu Maji, Alexander C. Berg
NIPS
2001
13 years 8 months ago
K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms
Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why K-Neares...
Pascal Vincent, Yoshua Bengio
NIPS
2003
13 years 8 months ago
Linear Response for Approximate Inference
Belief propagation on cyclic graphs is an efficient algorithm for computing approximate marginal probability distributions over single nodes and neighboring nodes in the graph. I...
Max Welling, Yee Whye Teh