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» A new generalization of the negative binomial distribution
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NIPS
1998
13 years 8 months ago
Controlling the Complexity of HMM Systems by Regularization
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting caused by insufficient training data. Regularization is done by augmenting the E...
Christoph Neukirchen, Gerhard Rigoll
IMC
2009
ACM
14 years 2 months ago
ANTIDOTE: understanding and defending against poisoning of anomaly detectors
Statistical machine learning techniques have recently garnered increased popularity as a means to improve network design and security. For intrusion detection, such methods build ...
Benjamin I. P. Rubinstein, Blaine Nelson, Ling Hua...
JMLR
2012
11 years 10 months ago
Max-Margin Min-Entropy Models
We propose a new family of latent variable models called max-margin min-entropy (m3e) models, which define a distribution over the output and the hidden variables conditioned on ...
Kevin Miller, M. Pawan Kumar, Benjamin Packer, Dan...
TCC
2010
Springer
179views Cryptology» more  TCC 2010»
14 years 4 months ago
Two Is a Crowd? A Black-Box Separation of One-Wayness and Security under Correlated Inputs
A family of trapdoor functions is one-way under correlated inputs if no efficient adversary can invert it even when given the value of the function on multiple correlated inputs. T...
Yevgeniy Vahlis
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
14 years 7 months ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto