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ECCV
2010
Springer
15 years 7 months 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
AROBOTS
2007
159views more  AROBOTS 2007»
15 years 6 months ago
Structure-based color learning on a mobile robot under changing illumination
— A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. To operate in the real world, autonomo...
Mohan Sridharan, Peter Stone
IDA
2007
Springer
15 years 6 months ago
Removing biases in unsupervised learning of sequential patterns
Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize th...
Yoav Horman, Gal A. Kaminka
IADIS
2004
15 years 8 months ago
Selecting Multimedia Interactions to Build Knowledge Structures
Two sets of multimedia learning materials were compared for their ability to promote learning of introductory computer programming The first set of materials was a sequentially na...
Wendy Doube, Juhani Tuovinen, Dale Shaffer
ICMCS
2005
IEEE
111views Multimedia» more  ICMCS 2005»
16 years 10 days ago
Manifold learning, a promised land or work in progress?
ABSTRACT In this paper, we report our experiments using a realworld image dataset to examine the effectiveness of Isomap, LLE and KPCA. The 1,897-image dataset we used consists of ...
Mei-Chen Yeh, I-Hsiang Lee, Gang Wu, Yi Wu, Edward...