Sciweavers

226 search results - page 1 / 46
» How to learn a graph from smooth signals
Sort
View
ICML
2007
IEEE
14 years 5 months ago
Constructing basis functions from directed graphs for value function approximation
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
Jeffrey Johns, Sridhar Mahadevan
UCS
2007
Springer
13 years 11 months ago
Discriminative Temporal Smoothing for Activity Recognition from Wearable Sensors
Abstract. This paper describes daily life activity recognition using wearable acceleration sensors attached to four different parts of the human body. The experimental data set con...
Jaakko Suutala, Susanna Pirttikangas, Juha Rö...
ICML
2010
IEEE
13 years 6 months ago
An Analysis of the Convergence of Graph Laplacians
Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumpti...
Daniel Ting, Ling Huang, Michael I. Jordan
CORR
2010
Springer
210views Education» more  CORR 2010»
13 years 5 months ago
Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in...
Tomer Faktor, Yonina C. Eldar, Michael Elad
ELPUB
1999
ACM
13 years 9 months ago
Learning Curves: Managing Smooth Product Development Cycles in Non-Print Environments
and abstract entry. But since the burgeoning of the scholarly literature since World War II, these processes had become well-known and expertly done by most organizations in the pu...
Jill O'Neill, Chris Leonard