Sciweavers

ICDM
2009
IEEE
92views Data Mining» more  ICDM 2009»
13 years 3 months ago
Semi-supervised Multi-task Learning with Task Regularizations
Multi-task learning refers to the learning problem of performing inference by jointly considering multiple related tasks. There have already been many research efforts on supervise...
Fei Wang, Xin Wang, Tao Li
RAS
2010
106views more  RAS 2010»
13 years 3 months ago
A developmental algorithm for ocular-motor coordination
This paper presents a model of ocular-motor development, inspired by ideas and data from developmental psychology. The learning problem concerns the growth of the transform betwee...
F. Chao, M. H. Lee, J. J. Lee
PAMI
2000
85views more  PAMI 2000»
13 years 5 months ago
Learning and Classification of Complex Dynamics
Ben North, Andrew Blake, Michael Isard, Jens Ritts...
JMLR
2008
131views more  JMLR 2008»
13 years 5 months ago
On Relevant Dimensions in Kernel Feature Spaces
We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert M...
ICML
2010
IEEE
13 years 6 months ago
Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences
This paper studies the problem of learning from ambiguous supervision, focusing on the task of learning semantic correspondences. A learning problem is said to be ambiguously supe...
Antoine Bordes, Nicolas Usunier, Jason Weston
ICML
2010
IEEE
13 years 6 months ago
SVM Classifier Estimation from Group Probabilities
A learning problem that has only recently gained attention in the machine learning community is that of learning a classifier from group probabilities. It is a learning task that ...
Stefan Rüping
NIPS
2001
13 years 6 months ago
Learning Lateral Interactions for Feature Binding and Sensory Segmentation
We present a new approach to the supervised learning of lateral interactions for the competitive layer model (CLM) dynamic feature binding architecture. The method is based on con...
Heiko Wersing
NIPS
2008
13 years 6 months ago
Structure Learning in Human Sequential Decision-Making
We use graphical models and structure learning to explore how people learn policies in sequential decision making tasks. Studies of sequential decision-making in humans frequently...
Daniel Acuña, Paul R. Schrater
NIPS
2008
13 years 6 months ago
Non-parametric Regression Between Manifolds
This paper discusses non-parametric regression between Riemannian manifolds. This learning problem arises frequently in many application areas ranging from signal processing, comp...
Florian Steinke, Matthias Hein
GOSLER
1995
13 years 8 months ago
Learning and Consistency
In designing learning algorithms it seems quite reasonable to construct them in such a way that all data the algorithm already has obtained are correctly and completely reflected...
Rolf Wiehagen, Thomas Zeugmann