Št. zadetkov: 11
Video in druga učna gradiva
Oznake:
computer science;machine learning
Leto:
2007
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
events
This proceedings is the published record of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-07) held in San Jose, California on August 12–15, 2007. The KDD-07 conference provides a forum for novel research results and important applications in the area ...
Leto:
2007
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning
Decision trees may be intelligible, but can they cut the mustard? Have SVMs replaced neural nets, or are neural nets still best for regression, and SVMs best for classification? Boosting maximizes a margin much like SVMs, but can boosting compete with SVMs? And is it better to boost weak models, as ...
Leto:
2005
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;data mining
Decision trees are intelligible, but do they perform well enough that you should use them? Have SVMs replaced neural nets, or are neural nets still best for regression, and SVMs best for classification? Boosting maximizes margins similar to SVMs, but can boosting compete with SVMs? And if it does co ...
Leto:
2007
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
Decision trees are intelligible, but do they perform well enough that you should use them? Have SVMs replaced neural nets, or are neural nets still best for regression, and SVMs best for classification? Boosting maximizes margins similar to SVMs, but can boosting compete with SVMs? And if it does co ...
Leto:
2007
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;data mining
Decision trees are intelligible, but do they perform well enough that you should use them? Have SVMs replaced neural nets, or are neural nets still best for regression, and SVMs best for classification? Boosting maximizes margins similar to SVMs, but can boosting compete with SVMs? And if it does co ...
Leto:
2007
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning
Decision trees are intelligible, but do they perform well enough that you should use them? Have SVMs replaced neural nets, or are neural nets still best for regression, and SVMs best for classification? Boosting maximizes margins similar to SVMs, but can boosting compete with SVMs? And if it does co ...
Leto:
2006
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;data science
In machine learning often a tradeoff must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible (e.g., deep nets, boosted trees, and random forests), and the most intelligible models usually are less accurate (e.g., linear or logistic regression). T ...
Leto:
2017
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;data science
Leto:
2017
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
In this paper, we address the semi-supervised learning problem when there is a small amount of labeled data augmented with pairwise constraints indicating whether a pair of examples belongs to a same class or different classes. We introduce a discriminative learning approach that incorporates pairwi ...
Leto:
2008
Vir:
videolectures.net