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» Defining the Goals to Optimise Data Mining Performance
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ICDM
2009
IEEE
175views Data Mining» more  ICDM 2009»
13 years 5 months ago
Maximum Margin Clustering with Multivariate Loss Function
This paper presents a simple but powerful extension of the maximum margin clustering (MMC) algorithm that optimizes multivariate performance measure specifically defined for clust...
Bin Zhao, James Tin-Yau Kwok, Changshui Zhang
ICDM
2009
IEEE
165views Data Mining» more  ICDM 2009»
13 years 5 months ago
Audio Classification of Bird Species: A Statistical Manifold Approach
Our goal is to automatically identify which species of bird is present in an audio recording using supervised learning. Devising effective algorithms for bird species classificati...
Forrest Briggs, Raviv Raich, Xiaoli Z. Fern
ICDM
2006
IEEE
182views Data Mining» more  ICDM 2006»
14 years 1 months ago
Active Learning to Maximize Area Under the ROC Curve
In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. ...
Matt Culver, Kun Deng, Stephen D. Scott
EDM
2009
114views Data Mining» more  EDM 2009»
13 years 5 months ago
Reducing the Knowledge Tracing Space
In Cognitive Tutors, student skill is represented by estimates of student knowledge on various knowledge components. The estimate for each knowledge component is based on a four-pa...
Steven Ritter, Thomas K. Harris, Tristan Nixon, Da...
PKDD
2010
Springer
235views Data Mining» more  PKDD 2010»
13 years 5 months ago
Online Structural Graph Clustering Using Frequent Subgraph Mining
The goal of graph clustering is to partition objects in a graph database into different clusters based on various criteria such as vertex connectivity, neighborhood similarity or t...
Madeleine Seeland, Tobias Girschick, Fabian Buchwa...