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AAAI
2011
12 years 6 months ago
Logistic Methods for Resource Selection Functions and Presence-Only Species Distribution Models
In order to better protect and conserve biodiversity, ecologists use machine learning and statistics to understand how species respond to their environment and to predict how they...
Steven Phillips, Jane Elith
CORR
2010
Springer
163views Education» more  CORR 2010»
13 years 4 months ago
Faster Rates for training Max-Margin Markov Networks
Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (M3 N) is an effective approach. All state-of-...
Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan
SIGIR
2008
ACM
13 years 6 months ago
Learning from labeled features using generalized expectation criteria
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
Gregory Druck, Gideon S. Mann, Andrew McCallum
WWW
2010
ACM
14 years 1 months ago
Factorizing personalized Markov chains for next-basket recommendation
Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn...
Steffen Rendle, Christoph Freudenthaler, Lars Schm...
ICC
2009
IEEE
107views Communications» more  ICC 2009»
14 years 1 months ago
Dynamic Resource Modeling for Heterogeneous Wireless Networks
— High variability of access resources in heterogenous wireless networks and limited computing power and battery life of mobile computing devices such as smartphones call for nov...
Dimitrios Tsamis, Tansu Alpcan, Jatinder Pal Singh...