Pinboard (jm)
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recent bookmarks from jmTop 10 data mining algorithms in plain English2015-05-18T10:24:03+00:00
http://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/#7_AdaBoost
jm1. C4.5
2. k-means
3. Support vector machines
4. Apriori
5. EM
6. PageRank
7. AdaBoost
8. kNN
9. Naive Bayes
10. CART
]]>svm k-means c4.5 apriori em pagerank adaboost knn naive-bayes cart ml data-mining machine-learning papers algorithms unsupervised supervisedhttps://pinboard.in/https://pinboard.in/u:jm/b:89faa089a8e3/'Poisoning Attacks against Support Vector Machines', Battista Biggio, Blaine Nelson, Pavel Laskov2012-07-23T20:19:09+00:00
http://arxiv.org/abs/1206.6389
jmWe investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM's test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes from a natural or well-behaved distribution. However, this assumption does not generally hold in security-sensitive settings. As we demonstrate, an intelligent adversary can, to some extent, predict the change of the SVM's decision function due to malicious input and use this ability to construct malicious data. The proposed attack uses a gradient ascent strategy in which the gradient is computed based on properties of the SVM's optimal solution. This method can be kernelized and enables the attack to be constructed in the input space even for non-linear kernels. We experimentally demonstrate that our gradient ascent procedure reliably identifies good local maxima of the non-convex validation error surface, which significantly increases the classifier's test error.
Via Alexandre Dulaunoy]]>papers svm machine-learning poisoning auto-learning security via:adulauhttps://pinboard.in/https://pinboard.in/u:jm/b:6bf8466662a2/