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» Learning Monotonic Linear Functions
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ICML
2007
IEEE
14 years 10 months ago
Information-theoretic metric learning
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative e...
Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit ...
ECAI
2008
Springer
13 years 11 months ago
Reinforcement Learning with the Use of Costly Features
In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. ...
Robby Goetschalckx, Scott Sanner, Kurt Driessens
RECOMB
2008
Springer
14 years 10 months ago
Automatic Parameter Learning for Multiple Network Alignment
We developed Gr?mlin 2.0, a new multiple network aligner with (1) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletion...
Jason Flannick, Antal F. Novak, Chuong B. Do, Bala...
GECCO
2007
Springer
181views Optimization» more  GECCO 2007»
14 years 1 months ago
Learning recursive programs with cooperative coevolution of genetic code mapping and genotype
The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively coevolves a population of adaptive mappings and associated genotypes is...
Garnett Carl Wilson, Malcolm I. Heywood
CC
2010
Springer
120views System Software» more  CC 2010»
13 years 7 months ago
Lower Bounds for Agnostic Learning via Approximate Rank
We prove that the concept class of disjunctions cannot be pointwise approximated by linear combinations of any small set of arbitrary real-valued functions. That is, suppose that t...
Adam R. Klivans, Alexander A. Sherstov