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PKDD
2015
Springer
12views Data Mining» more  PKDD 2015»
4 years 10 months ago
Scalable Metric Learning for Co-Embedding
We present a general formulation of metric learning for co-embedding, where the goal is to relate objects from different sets. The framework allows metric learning to be applied to...
Farzaneh Mirzazadeh, Martha White, András G...
PKDD
2015
Springer
23views Data Mining» more  PKDD 2015»
4 years 10 months ago
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
Abstract. We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). ...
David Tolpin, Jan-Willem van de Meent, Brooks Paig...
PKDD
2015
Springer
11views Data Mining» more  PKDD 2015»
4 years 10 months ago
Superset Learning Based on Generalized Loss Minimization
In standard supervised learning, each training instance is associated with an outcome from a corresponding output space (e.g., a class label in classification or a real number in ...
Eyke Hüllermeier, Weiwei Cheng
PKDD
2015
Springer
7views Data Mining» more  PKDD 2015»
4 years 10 months ago
Quantifying Energy Demand in Mountainous Areas
Abstract. Despite their rich energy renewable potential, mountainous areas suffer from energy poverty. A viable solution seems to be the radical turn towards renewable resources. A...
Lefkothea Papada, Dimitrios Kaliampakos
PKDD
2015
Springer
9views Data Mining» more  PKDD 2015»
4 years 10 months ago
Dyad Ranking Using A Bilinear Plackett-Luce Model
Dirk Schäfer, Eyke Hüllermeier
PKDD
2015
Springer
16views Data Mining» more  PKDD 2015»
4 years 10 months ago
A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons
We consider the problem of clustering a given dataset into k clusters subject to an additional set of constraints on relative distance comparisons between the data items. The addit...
Ehsan Amid, Aristides Gionis, Antti Ukkonen
PKDD
2015
Springer
15views Data Mining» more  PKDD 2015»
4 years 10 months ago
Semi-supervised Subspace Co-Projection for Multi-class Heterogeneous Domain Adaptation
Heterogeneous domain adaptation aims to exploit labeled training data from a source domain for learning prediction models in a target domain under the condition that the two domain...
Min Xiao, Yuhong Guo
PKDD
2015
Springer
9views Data Mining» more  PKDD 2015»
4 years 10 months ago
Handling Oversampling in Dynamic Networks Using Link Prediction
Abstract. Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little wor...
Benjamin Fish, Rajmonda S. Caceres
PKDD
2015
Springer
5views Data Mining» more  PKDD 2015»
4 years 10 months ago
Probabilistic Programming in Anglican
Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We describe the implementation of Anglican and illustrate how its desi...
David Tolpin, Jan-Willem van de Meent, Frank Wood
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