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ETVC
2008
15 years 8 months ago
Intrinsic Geometries in Learning
In a seminal paper, Amari (1998) proved that learning can be made more efficient when one uses the intrinsic Riemannian structure of the algorithms' spaces of parameters to po...
Richard Nock, Frank Nielsen
EDM
2008
193views Data Mining» more  EDM 2008»
15 years 8 months ago
Can we predict which groups of questions students will learn from?
In a previous study ([4]), we used the ASSISTment system to track student knowledge longitudinally over the course of a schools year, based upon each student using our system about...
Mingyu Feng, Neil T. Heffernan, Joseph E. Beck, Ke...
NIPS
2004
15 years 8 months ago
Schema Learning: Experience-Based Construction of Predictive Action Models
Schema learning is a way to discover probabilistic, constructivist, predictive action models (schemas) from experience. It includes methods for finding and using hidden state to m...
Michael P. Holmes, Charles Lee Isbell Jr.
UAI
2003
15 years 7 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
ICML
2010
IEEE
15 years 7 months ago
Gaussian Processes Multiple Instance Learning
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Minyoung Kim, Fernando De la Torre