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SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
11 years 7 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman
JAIR
2006
105views more  JAIR 2006»
13 years 4 months ago
Active Learning with Multiple Views
Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on...
Ion Muslea, Steven Minton, Craig A. Knoblock
ECCC
2006
96views more  ECCC 2006»
13 years 4 months ago
When Does Greedy Learning of Relevant Features Succeed? --- A Fourier-based Characterization ---
Detecting the relevant attributes of an unknown target concept is an important and well studied problem in algorithmic learning. Simple greedy strategies have been proposed that s...
Jan Arpe, Rüdiger Reischuk
IJCAI
2007
13 years 6 months ago
Searching for Interacting Features
Feature interaction presents a challenge to feature selection for classification. A feature by itself may have little correlation with the target concept, but when it is combined...
Zheng Zhao, Huan Liu
ICMLA
2007
13 years 6 months ago
Phase transition and heuristic search in relational learning
Several works have shown that the covering test in relational learning exhibits a phase transition in its covering probability. It is argued that this phase transition dooms every...
Érick Alphonse, Aomar Osmani
COLT
1995
Springer
13 years 8 months ago
Learning with Unreliable Boundary Queries
We introduce a new model for learning with membership queries in which queries near the boundary of a target concept may receive incorrect or “don’t care” responses. In part...
Avrim Blum, Prasad Chalasani, Sally A. Goldman, Do...
ECML
2003
Springer
13 years 9 months ago
Robust k-DNF Learning via Inductive Belief Merging
A central issue in logical concept induction is the prospect of inconsistency. This problem may arise due to noise in the training data, or because the target concept does not fit...
Frédéric Koriche, Joël Quinquet...
KR
2004
Springer
13 years 10 months ago
GlossOnt: A Concept-focused Ontology Building Tool
The demand for ontologies is rapidly growing especially due to developments in knowledge management, E-commerce and the Semantic Web. Building an ontology and a background knowled...
Youngja Park
AUSAI
2005
Springer
13 years 10 months ago
Locating Regions of Interest in CBIR with Multi-instance Learning Techniques
In content-based image retrieval (CBIR), the user usually poses several labelled images and then the system attempts to retrieve all the images relevant to the target concept defi...
Zhi-Hua Zhou, Xiao-Bing Xue, Yuan Jiang
ICMCS
2007
IEEE
154views Multimedia» more  ICMCS 2007»
13 years 10 months ago
Improving Semantic Concept Detection and Retrieval using Contextual Estimates
In this paper we introduce a novel contextual fusion method to improve the detection scores of semantic concepts in images and videos. Our method consists of three phases. For eac...
Yusuf Aytar, Omer Bilal Orhan, Mubarak Shah