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» Modeling Problem Transformations based on Data Complexity
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EPIA
2003
Springer
15 years 11 months ago
Adaptation to Drifting Concepts
Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an ex...
Gladys Castillo, João Gama, Pedro Medas
AUTOMATICA
2006
132views more  AUTOMATICA 2006»
15 years 6 months ago
A new autocovariance least-squares method for estimating noise covariances
Industrial implementation of model-based control methods, such as model predictive control, is often complicated by the lack of knowledge about the disturbances entering the syste...
Brian J. Odelson, Murali R. Rajamani, James B. Raw...
ICDM
2010
IEEE
122views Data Mining» more  ICDM 2010»
15 years 4 months ago
Learning Preferences with Millions of Parameters by Enforcing Sparsity
We study the retrieval task that ranks a set of objects for a given query in the pairwise preference learning framework. Recently researchers found out that raw features (e.g. word...
Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, Jaime G....
ICCV
2011
IEEE
14 years 6 months ago
Dynamic Manifold Warping for View Invariant Action Recognition
We address the problem of learning view-invariant 3D models of human motion from motion capture data, in order to recognize human actions from a monocular video sequence with arbi...
Dian Gong, Gerard Medioni
ICML
2003
IEEE
16 years 7 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
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