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ICML
2004
IEEE
14 years 3 months ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
ILP
2004
Springer
14 years 3 months ago
Learning an Approximation to Inductive Logic Programming Clause Evaluation
One challenge faced by many Inductive Logic Programming (ILP) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as ...
Frank DiMaio, Jude W. Shavlik
ICPR
2010
IEEE
14 years 3 months ago
Motif Discovery and Feature Selection for CRF-Based Activity Recognition
Abstract—Due to their ability to model sequential data without making unnecessary independence assumptions, conditional random fields (CRFs) have become an increasingly popular ...
Liyue Zhao, Xi Wang, Gita Sukthankar
EVOW
2003
Springer
14 years 3 months ago
Landscape State Machines: Tools for Evolutionary Algorithm Performance Analyses and Landscape/Algorithm Mapping
Abstract. Many evolutionary algorithm applications involve either fitness functions with high time complexity or large dimensionality (hence very many fitness evaluations will typi...
David Corne, Martin J. Oates, Douglas B. Kell
ISAAC
2003
Springer
115views Algorithms» more  ISAAC 2003»
14 years 3 months ago
A Faster Lattice Reduction Method Using Quantum Search
We propose a new lattice reduction method. Our algorithm approximates shortest lattice vectors up to a factor ≤ (k/6)n/2k and makes use of Grover’s quantum search algorithm. Th...
Christoph Ludwig