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» Evaluating algorithms that learn from data streams
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DKE
2006
110views more  DKE 2006»
15 years 6 months ago
Efficient mining of group patterns from user movement data
In this paper, we present a new approach to derive groupings of mobile users based on their movement data. We assume that the user movement data are collected by logging location ...
Yida Wang, Ee-Peng Lim, San-Yih Hwang
ICML
2010
IEEE
15 years 4 months ago
Approximate Predictive Representations of Partially Observable Systems
We provide a novel view of learning an approximate model of a partially observable environment from data and present a simple implemenf the idea. The learned model abstracts away ...
Monica Dinculescu, Doina Precup
CVPR
2010
IEEE
15 years 5 months ago
Rapid and accurate developmental stage recognition of C. elegans from high-throughput image data
We present a hierarchical principle for object recognition and its application to automatically classify developmental stages of C. elegans animals from a population of mixed stag...
Amelia White, Huey-Ling Kao, Patricia Cipriani, Br...
CF
2010
ACM
15 years 9 months ago
Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware
Computational neuroscience is being revolutionized with the advent of multi-electrode arrays that provide real-time, dynamic perspectives into brain function. Mining neuronal spik...
Yong Cao, Debprakash Patnaik, Sean P. Ponce, Jerem...
VLSISP
2011
358views Database» more  VLSISP 2011»
15 years 1 months ago
Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition
Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from superlinear increases in ...
Karthik Nagarajan, Brian Holland, Alan D. George, ...