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DAWAK
2006
Springer
14 years 9 days ago
Learning Classifiers from Distributed, Ontology-Extended Data Sources
Abstract. There is an urgent need for sound approaches to integrative and collaborative analysis of large, autonomous (and hence, inevitably semantically heterogeneous) data source...
Doina Caragea, Jun Zhang 0002, Jyotishman Pathak, ...
IJCAI
2007
13 years 10 months ago
Learning to Identify Unexpected Instances in the Test Set
Traditional classification involves building a classifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instan...
Xiaoli Li, Bing Liu, See-Kiong Ng
AMI
2010
Springer
13 years 6 months ago
Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City
Knowing where vacant taxis are and will be at a given time and location helps the users in daily planning and scheduling, as well as the taxi service providers in dispatching. In t...
Santi Phithakkitnukoon, Marco Veloso, Carlos Bento...
JIPS
2007
92views more  JIPS 2007»
13 years 8 months ago
Optimization of Domain-Independent Classification Framework for Mood Classification
In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naïve Bayesian classification algorithms. The architecture of our s...
Sung-Pil Choi, Yuchul Jung, Sung-Hyon Myaeng
CVPR
2008
IEEE
14 years 10 months ago
In defense of Nearest-Neighbor based image classification
State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric Nearest-Neighbor (NN) based imag...
Oren Boiman, Eli Shechtman, Michal Irani