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» Evaluating algorithms that learn from data streams
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ICML
2002
IEEE
16 years 8 months ago
Multi-Instance Kernels
Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently, rese...
Adam Kowalczyk, Alex J. Smola, Peter A. Flach, Tho...
ICML
2002
IEEE
16 years 8 months ago
Learning to Share Distributed Probabilistic Beliefs
In this paper, we present a general machine learning approach to the problem of deciding when to share probabilistic beliefs between agents for distributed monitoring. Our approac...
Christopher Leckie, Kotagiri Ramamohanarao
CBMS
2006
IEEE
16 years 1 months ago
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning sys...
Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen...
AAAI
1993
15 years 8 months ago
Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning
learning (EBL) component. In this paper we provide a brief review of FOIL and FOCL, then discuss how operationalizing a domain theory can adversely affect the accuracy of a learned...
Michael J. Pazzani, Clifford Brunk
ICDM
2009
IEEE
137views Data Mining» more  ICDM 2009»
16 years 1 months ago
Regression Learning Vector Quantization
— Learning Vector Quantization (LVQ) is a popular class of nearest prototype classifiers for multiclass classification. Learning algorithms from this family are widely used becau...
Mihajlo Grbovic, Slobodan Vucetic