jm + svm   2

Top 10 data mining algorithms in plain English
This is a phenomenally useful ML/data-mining resource post -- 'the top 10 most influential data mining algorithms as voted on by 3 separate panels in [ICDM '06's] survey paper', but with a nice clear intro and description for each one. Here's the algorithms covered:
1. 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  supervised 
may 2015 by jm
'Poisoning Attacks against Support Vector Machines', Battista Biggio, Blaine Nelson, Pavel Laskov
The perils of auto-training SVMs on unvetted input.
We 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:adulau 
july 2012 by jm

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