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» Learning from Highly Structured Data by Decomposition
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CVPR
2007
IEEE
16 years 3 days ago
Fast Terrain Classification Using Variable-Length Representation for Autonomous Navigation
We propose a method for learning using a set of feature representations which retrieve different amounts of information at different costs. The goal is to create a more efficient ...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...
SMC
2007
IEEE
130views Control Systems» more  SMC 2007»
15 years 4 months ago
A flow based approach for SSH traffic detection
— The basic objective of this work is to assess the utility of two supervised learning algorithms AdaBoost and RIPPER for classifying SSH traffic from log files without using f...
Riyad Alshammari, A. Nur Zincir-Heywood
NN
2006
Springer
140views Neural Networks» more  NN 2006»
14 years 10 months ago
Neural mechanism for stochastic behaviour during a competitive game
Previous studies have shown that non-human primates can generate highly stochastic choice behaviour, especially when this is required during a competitive interaction with another...
Alireza Soltani, Daeyeol Lee, Xiao-Jing Wang
NIPS
1998
14 years 11 months ago
Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations
A common way to represent a time series is to divide it into shortduration blocks, each of which is then represented by a set of basis functions. A limitation of this approach, ho...
Michael S. Lewicki, Terrence J. Sejnowski
EGPGV
2011
Springer
366views Visualization» more  EGPGV 2011»
14 years 1 months ago
GPU Algorithms for Diamond-based Multiresolution Terrain Processing
We present parallel algorithms for processing, extracting and rendering adaptively sampled regular terrain datasets represented as a multiresolution model defined by a super-squa...
M. Adil Yalçin, Kenneth Weiss, Leila De Flo...