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» Evaluating algorithms that learn from data streams
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ICML
2002
IEEE
16 years 7 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 7 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 19 days 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 7 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