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JMLR
2010
121views more  JMLR 2010»
15 years 1 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
JMLR
2010
185views more  JMLR 2010»
15 years 1 months ago
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
We introduce a simple order-based greedy heuristic for learning discriminative structure within generative Bayesian network classifiers. We propose two methods for establishing an...
Franz Pernkopf, Jeff A. Bilmes
JMLR
2010
129views more  JMLR 2010»
15 years 1 months ago
Expectation Truncation and the Benefits of Preselection In Training Generative Models
We show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (...
Jörg Lücke, Julian Eggert
JSS
2010
198views more  JSS 2010»
15 years 1 months ago
Accelerated collection of sensor data by mobility-enabled topology ranks
We study the problem of fast and energy-efficient data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Mot...
Constantinos Marios Angelopoulos, Sotiris E. Nikol...
MEMICS
2010
15 years 1 months ago
A Simple Topology Preserving Max-Flow Algorithm for Graph Cut Based Image Segmentation
In this paper, we propose a modification to the Boykov-Kolmogorov maximum flow algorithm [2] in order to make the algorithm preserve the topology of an initial interface. This alg...
Ondrej Danek, Martin Maska
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