SVM Recommended reading literature and blogs

SVM is a classic classification algorithm, the network has a lot of wonderful blog and books to explain, today I put together these materials, thanks to the share of Daniel!

[1] Jerrylead's blog, the author gives a fluent and popular derivation according to Stanford's handouts: the SVM series.

[2] Carlsberg's introductory series of SVM speaks very well.

[3] Pluskid's support vector machine series, very good. The derivation of the dual problem is very good.

[4] Leo Zhang's SVM Learning series, which includes many other machine learning algorithms, is also included in the blog.

[5] V_july_v's popular introduction to support vector machines (understanding the three-tier realm of SVM). The method of the algorithm of the structure of the blog.

[6] Hangyuan Li's "Statistical learning Method", Tsinghua University Press

[7] SVM learning--sequential Minimal optimization

[8] SVM algorithm implementation (i.)

[9] Sequential Minimal optimization:a fastalgorithm for Training support Vector machines

[Ten] SVM--from "principle" to realization

[11] Support Vector Machine Starter Series

[12] Various versions of SVM and its multiple language implementation code collection

[Karush-kuhn-tucker] (KKT) conditions

[14] In-depth understanding of Lagrange multiplier method (Lagrange Multiplier) and Kkt conditions

[15] machine learning algorithms and Python Practice

SVM Recommended reading literature and blogs