Št. zadetkov: 16
Video in druga učna gradiva
Oznake:
computer science;machine learning;clustering
Leto:
2007
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;computational learning theory
Leto:
2006
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning
Theoretical analysis has played a major role in some of the most prominent practical successes of statistical machine learning. However, mainstream machine learning theory assumes some strong simplifying assumptions which are often unrealistic. In the past decade, the practice of machine learning ha ...
Leto:
2009
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning
Machine learning enjoys a deep and powerful theory that has led to a wide variety of highly successful practical tools. However, most of this theory is developed under some simplifying assumptions that clearly fail in the real world. In particular, a fundamental assumption of the theory is that the ...
Leto:
2009
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;clustering
Consider the task of clustering university web pages based on the graph of links between these pages. Can clusters of "functionally similar" pages be detected from just this link structure? Note that this is a clustering task in which one starts without any prior knowledge of any similarity or dista ...
Leto:
2010
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;kernel methods
The success of kernel based learning algorithms depends upon the suitability of the kernel to the learning
task. Ideally, the choice of a kernel should based on prior information of the learner about the task at hand.
However, in practice, kernel parameters are being tuned based on available train ...
Leto:
2008
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;structured data
I will present a formal framework for transfer learning and investigate under which conditions is it possible to provide performance guarantees for such scenarios. I will address two key issues:
*1) Which notions of task-similarity suffice to provide meaningful error bounds on a target task, for a p ...
Leto:
2009
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
We consider the situation in which there is a basic learning task but different sub-tasks
define different data generating distributions. Examples include learning to identify
spam for various different email users, or parts-of-speech tagging for different text
corpora. Our goal is to allow the u ...
Leto:
2006
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning
The talk introduces a new framework for learning probability density functions by assessing their performance against a set of 'test functions'.
A theoretical analysis suggests that we can tailor a distribution for a class of tasks by training it to fit a small subsample. There is a trade-off betwe ...
Leto:
2007
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
mathematics
We propose a 'subjective' way of defining similarity between probability distributions.Our measure is parameterized by a collection H of subsets of the domain over which the probability distributions are defined. Intuitively speaking, H is the collection of 'subsets of interest' with respect to thep ...
Leto:
2007
Vir:
videolectures.net