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ICML
2004
IEEE
14 years 1 months ago
Towards tight bounds for rule learning
While there is a lot of empirical evidence showing that traditional rule learning approaches work well in practice, it is nearly impossible to derive analytical results about thei...
Ulrich Rückert, Stefan Kramer
SAC
2006
ACM
14 years 1 months ago
Discretization from data streams: applications to histograms and data mining
Abstract. In this paper we propose a new method to perform incremental discretization. The basic idea is to perform the task in two layers. The first layer receives the sequence o...
João Gama, Carlos Pinto
CVPR
2009
IEEE
15 years 2 months ago
Learning a Distance Metric from Multi-instance Multi-label Data
Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. ...
Rong Jin (Michigan State University), Shijun Wang...
KDD
2002
ACM
96views Data Mining» more  KDD 2002»
14 years 8 months ago
A theoretical framework for learning from a pool of disparate data sources
Shai Ben-David, Johannes Gehrke, Reba Schuller
ICALT
2006
IEEE
14 years 1 months ago
Towards Effective Usage-Based Learning Applications: Track and Learn from User Experience(s)
In this paper we propose a schema and framework for recording and managing attention metadata. This framework is intended to capture, manage, and re-use data about attention users...
Jehad Najjar, Erik Duval, Martin Wolpers