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Št. zadetkov: 5
Video in druga učna gradiva
Oznake: computer science;machine learning
We present tight surrogate regret bounds for the class of proper (i.e., Fisher consistent) losses. The bounds generalise the margin-based bounds due to Bartlett et al. (2006). The proof uses Taylor's theorem and leads to new representations for loss and regret and a simple proof of the integral repr ...
Leto: 2009 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computer science;machine learning
In order to relate machine learning problems we argue that we need to be able to articulate what is meant by a single machine learning problem. By attempting to name the various aspects of a learning problem we hope to clarify ways in which learning problems might be related to each other. We tentat ...
Leto: 2011 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computer science;machine learning
This course highlights some relationships between surrogate losses, scoring rules, f-divergences, Bregman divergences, statistical information and ROC curves and their implications for applications such as divergence estimation.
Leto: 2009 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computer science;machine learning;active learning;computational learning theory;on-line learning;reinforcement learning;semi-supervised learning
Mixability is a property of a loss which characterizes when fast convergence is possible in the game of prediction with expert advice. We show that a key property of mixability generalizes, and the $\exp$ and $\log$ operations present in the usual theory are not as special as one might have thought. ...
Leto: 2015 Vir: videolectures.net
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Oznake: marketing;trženje;informacije;informacijski viri;elektronsko poslovanje;kreativnost;ustvarjalnost;strategija;poslovni proces;sposobnosti;znanje;mišljenje;image;
Your first year in network marketing : overcome your fears, experience success, and achieve your dreams!
Leto: 1998 Vir: Ekonomsko-poslovna fakulteta (UM EPF)
Št. zadetkov: 5
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