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ECCV
2010
Springer
15 years 19 days ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
95
Voted
ICDCS
2009
IEEE
15 years 9 months ago
CLIQUE: Role-Free Clustering with Q-Learning for Wireless Sensor Networks
Clustering and aggregation inherently increase wireless sensor network (WSN) lifetime by collecting information within a cluster at a cluster head, reducing the amount of data thr...
Anna Förster, Amy L. Murphy
FCCM
2002
IEEE
146views VLSI» more  FCCM 2002»
15 years 5 months ago
Fast Area Estimation to Support Compiler Optimizations in FPGA-Based Reconfigurable Systems
Several projects have developed compiler tools that translate high-level languages down to hardware description languages for mapping onto FPGAbased reconfigurable computers. Thes...
Dhananjay Kulkarni, Walid A. Najjar, Robert Rinker...
129
Voted
ISVC
2010
Springer
14 years 11 months ago
Using R-Trees for Interactive Visualization of Large Multidimensional Datasets
Large, multidimensional datasets are difficult to visualize and analyze. Visualization interfaces are constrained in resolution and dimension, so cluttering and problems of project...
Alfredo Giménez, René Rosenbaum, Mar...
CISS
2008
IEEE
15 years 7 months ago
Distributed processing in frames for sparse approximation
—Beyond signal processing applications, frames are also powerful tools for modeling the sensing and information processing of many biological and man-made systems that exhibit in...
Christopher J. Rozell