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» Learning Linearly Separable Languages
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PLDI
2011
ACM
12 years 11 months ago
Separation logic + superposition calculus = heap theorem prover
Program analysis and verification tools crucially depend on the ability to symbolically describe and reason about sets of program behaviors. Separation logic provides a promising...
Juan Antonio Navarro Pérez, Andrey Rybalche...
ECML
2007
Springer
14 years 2 months ago
Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models
Abstract. Robustly estimating the state-transition probabilities of highorder Markov processes is an essential task in many applications such as natural language modeling or protei...
Rikiya Takahashi
MLDM
2005
Springer
14 years 2 months ago
Linear Manifold Clustering
In this paper we describe a new cluster model which is based on the concept of linear manifolds. The method identifies subsets of the data which are embedded in arbitrary oriented...
Robert M. Haralick, Rave Harpaz
ICML
2003
IEEE
14 years 9 months ago
Probabilistic Classifiers and the Concepts They Recognize
We investigate algebraic, logical, and geometric properties of concepts recognized by various classes of probabilistic classifiers. For this we introduce a natural hierarchy of pr...
Manfred Jaeger
CIE
2007
Springer
14 years 2 months ago
Circuit Complexity of Regular Languages
We survey our current knowledge of circuit complexity of regular languages and we prove that regular languages that are in AC0 and ACC0 are all computable by almost linear size ci...
Michal Koucký