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ICML
2004
IEEE
16 years 1 months ago
Support vector machine learning for interdependent and structured output spaces
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input...
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten J...
88
Voted
PVM
2009
Springer
15 years 7 months ago
Optimizing MPI Runtime Parameter Settings by Using Machine Learning
Abstract. Manually tuning MPI runtime parameters is a practice commonly employed to optimise MPI application performance on a specific architecture. However, the best setting for ...
Simone Pellegrini, Jie Wang, Thomas Fahringer, Han...
FPL
2000
Springer
155views Hardware» more  FPL 2000»
15 years 4 months ago
Synthesis and Implementation of RAM-Based Finite State Machines in FPGAs
This paper discusses the design and implementation of finite state machines (FSM) with combinational circuits that are built primarily from RAM blocks. It suggests a novel state as...
Valery Sklyarov
117
Voted
ESA
2001
Springer
145views Algorithms» more  ESA 2001»
15 years 5 months ago
Grouping Techniques for Scheduling Problems: Simpler and Faster
In this paper we describe a general grouping technique to devise faster and simpler approximation schemes for several scheduling problems. We illustrate the technique on two diff...
Aleksei V. Fishkin, Klaus Jansen, Monaldo Mastroli...
ICML
2010
IEEE
15 years 1 months ago
Simple and Efficient Multiple Kernel Learning by Group Lasso
We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL). In literature, MKL is often solved by an alternating approach: (1) the minimization of ...
Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Mic...