1. LIBLINEAR IN 20 MINS
Chandler Huang
previa [at] gmail.com

2. Liblinear
 SVM:
 Looking for a hyper-plane to separate sample
data
 SVR:
 Looking for a hyper-plane to predict data
distribution
 Example:
PASS Grade w1 w2 w3 w4
T 95 4.7 118 1M 172
T 70 3 121 1.2M 181
F 55 3.6 102 0.8M 173
F 48 2.7 108 0.85M 183

3. Liblinear
 Both solve
 with different

4. Python wrapper of Liblinear
 liblinear.py
 liblinear = CDLL(path.join(dirname,
'../liblinear.so.1'))
 Class: feature_node, problem, parameter, model
 liblinearutil.py
 import liblinear
 load/save_model(), evaluations(), train(), predict()

5. SOP
 Text classification
 Text segmentation
 Feature selection
 Train model
 Verify testing data

6. SOP
 Text classification
 Text segmentation
 N-Gram, HMM
 Segmentor for Python (Opensource)
 囉嗦(Loso)
 http://opensource.plurk.com/Loso_Chinese_Segmenta
tion_System/
 結巴(jieba)
 https://github.com/fxsjy/jieba
 Smallseg
 https://code.google.com/p/smallseg/

7. SOP
 Text classification
 Feature selection
 Garbage in garbage out
 EX: Wiki title index
 http://dumps.wikimedia.org/zhwiktionary/
 Libsvm vs Liblinear
 Libsvm:O(n2) or O(n3)
 Liblinear: O(n)
 in practice libsvm becomes painfully slow at 10k samples.
 http://tinyurl.com/ke4btjv

8. SOP
 Text classification
 Train
 Format
 Solver type(default 1)
0 -- L2-regularized logistic regression (primal)
1 -- L2-regularized L2-loss support vector classification
(dual)
2 -- L2-regularized L2-loss support vector classification
(primal)
3 -- L2-regularized L1-loss support vector classification
(dual)
4 -- support vector classification by Crammer and Singer
5 -- L1-regularized L2-loss support vector classification

9. SOP
 Text classification
 Train
 -c cost
 set the parameter C (default 1)
 -p epsilon
 set the epsilon in loss function of epsilon-SVR (default 0.1)
 -e epsilon
 set tolerance of termination criterion
 -B bias
 if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
 -wi weight
 weights adjust the parameter C of different classes (see README for details)
 -v n
 n-fold cross validation mode
 -q
 quiet mode (no outputs)

10. SOP
 Text classification
 Verify testing data
 Using predict()

11. LIVE DEMO

12. Reference
 LIBLINEAR
 A Library for Large Linear Classication
 http://www.csie.ntu.edu.tw/~cjlin/papers/liblinear.pdf
 L1, L2-Regularization
 L1 vs. L2 Regularization and feature selection
 http://cs.nyu.edu/~rostami/presentations/L1_vs_L2.pdf
 L1-norm Regularization
 http://cseweb.ucsd.edu/~saul/teaching/cse291s07/L1norm.
pdf
 Sparsity and Some Basics of L1 Regularization
 http://freemind.pluskid.org/machine-learning/sparsity-and-
some-basics-of-l1-regularization/

13. Reference
 Segmentor
 四款python中文分词系统简单测试
 http://hi.baidu.com/fooying/item/6ae7a0e26087e8d7eb
34c9e8
 MMSEG
 http://technology.chtsai.org/mmseg/
 開源中國，中文分詞庫
 http://tinyurl.com/k564x9k

14. THANKS