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» A theory of learning with similarity functions
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ICML
2003
IEEE
14 years 5 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
JAIR
2008
135views more  JAIR 2008»
13 years 4 months ago
On Similarities between Inference in Game Theory and Machine Learning
In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two dom...
Iead Rezek, David S. Leslie, Steven Reece, Stephen...
ICML
2006
IEEE
14 years 5 months ago
Ranking on graph data
In ranking, one is given examples of order relationships among objects, and the goal is to learn from these examples a real-valued ranking function that induces a ranking or order...
Shivani Agarwal
ECML
2006
Springer
13 years 8 months ago
(Agnostic) PAC Learning Concepts in Higher-Order Logic
This paper studies the PAC and agnostic PAC learnability of some standard function classes in the learning in higher-order logic setting introduced by Lloyd et al. In particular, i...
Kee Siong Ng
ICML
2007
IEEE
14 years 5 months ago
Learning distance function by coding similarity
We consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. 'similar' point pairs. We define the similarity in informa...
Aharon Bar-Hillel, Daphna Weinshall