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» Semi-supervised Learning from General Unlabeled Data
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ICRA
2005
IEEE
122views Robotics» more  ICRA 2005»
14 years 1 months ago
Supervised Learning of Places from Range Data using AdaBoost
— This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of pla...
Óscar Martínez Mozos, Cyrill Stachni...
AIRS
2010
Springer
13 years 5 months ago
Semantic Relation Extraction Based on Semi-supervised Learning
Many tasks of information extraction or natural language processing have a property that the data naturally consist of several views--disjoint subsets of features. Specifically, a ...
Haibo Li, Yutaka Matsuo, Mitsuru Ishizuka
GECCO
2009
Springer
204views Optimization» more  GECCO 2009»
14 years 13 days ago
Combined structure and motion extraction from visual data using evolutionary active learning
We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are c...
Krishnanand N. Kaipa, Josh C. Bongard, Andrew N. M...
TCS
2011
13 years 2 months ago
Smart PAC-learners
The PAC-learning model is distribution-independent in the sense that the learner must reach a learning goal with a limited number of labeled random examples without any prior know...
Malte Darnstädt, Hans-Ulrich Simon
MM
2006
ACM
218views Multimedia» more  MM 2006»
14 years 1 months ago
SmartLabel: an object labeling tool using iterated harmonic energy minimization
Labeling objects in images is an essential prerequisite for many visual learning and recognition applications that depend on training data, such as image retrieval, object detecti...
Wen Wu, Jie Yang