Update README

diff --git a/README b/README
index 63b5b366fd8a43282286fe251f6d49807dee0b2e..57293f1c3bdad5c7f30108f02caf52ed70a7e085 100644 (file)
--- a/README
+++ b/README
@@ -1,7 +1,8 @@
 LIBLINEAR is a simple package for solving large-scale regularized
 linear classification. It currently supports L2-regularized logistic
-regression, L2-loss support vector machines, and L1-loss support
-vector machines. This document explains the usage of LIBLINEAR.
+regression/L2-loss support vector classification/L1-loss support vector
+classification, and L1-regularized L2-loss support vector classification/
+logistic regression. This document explains the usage of LIBLINEAR.
 
 To get started, please read the ``Quick Start'' section first.
 For developers, please check the ``Library Usage'' section to learn
@@ -96,10 +97,12 @@ Usage: train [options] training_set_file [model_file]
 options:
 -s type : set type of solver (default 1)
         0 -- L2-regularized logistic regression
-        1 -- L2-loss support vector machines (dual)
-        2 -- L2-loss support vector machines (primal)
-        3 -- L1-loss support vector machines (dual)
-        4 -- multi-class support vector machines by Crammer and Singer
+        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 -- multi-class support vector classification by Crammer and Singer
+        5 -- L1-regularized L2-loss support vector classification
+        6 -- L1-regularized logistic regression
 -c cost : set the parameter C (default 1)
 -e epsilon : set tolerance of termination criterion
         -s 0 and 2
@@ -108,6 +111,9 @@ options:
                positive/negative data (default 0.01)
         -s 1, 3, and 4
                 Dual maximal violation <= eps; similar to libsvm (default 0.1)
+        -s 5 and 6
+                |f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf,
+        where f is the primal function (default 0.01)
 -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
@@ -122,20 +128,28 @@ For L2-regularized logistic regression (-s 0), we solve
 
 min_w w^Tw/2 + C \sum log(1 + exp(-y_i w^Tx_i))
 
-For L2-loss SVM dual (-s 1), we solve
+For L2-regularized L2-loss SVC dual (-s 1), we solve
 
 min_alpha  0.5(alpha^T (Q + I/2/C) alpha) - e^T alpha 
     s.t.   0 <= alpha_i,
 
-For L2-loss SVM (-s 2), we solve
+For L2-regularized L2-loss SVC (-s 2), we solve
 
 min_w w^Tw/2 + C \sum max(0, 1- y_i w^Tx_i)^2
 
-For L1-loss SVM dual (-s 3), we solve
+For L2-regularized L1-loss SVC dual (-s 3), we solve
 
 min_alpha  0.5(alpha^T Q alpha) - e^T alpha 
     s.t.   0 <= alpha_i <= C,
 
+For L1-regularized L2-loss SVC (-s 5), we solve
+
+min_w \sum |w_j| + C \sum max(0, 1- y_i w^Tx_i)^2
+
+For L1-regularized logistic regression (-s 6), we solve
+
+min_w \sum |w_j| + C \sum log(1 + exp(-y_i w^Tx_i))
+
 where
 
 Q is a matrix with Q_ij = y_i y_j x_i^T x_j.
@@ -273,13 +287,15 @@ Library Usage
                 double* weight;
         };
 
-    solver_type can be one of L2_LR, L2LOSS_SVM_DUAL, L2LOSS_SVM, L1LOSS_SVM_DUAL, MCSVM_CS.
+    solver_type can be one of L2_LR, L2_L2LOSS_SVC_DUAL, L2_L2LOSS_SVC, L2_L1LOSS_SVC_DUAL, MCSVM_CS, L1_L2LOSS_SVC, L1_LR.
 
-    L2_LR               L2-regularized logistic regression
-    L2LOSS_SVM_DUAL     L2-loss support vector machines (dual) 
-    L2LOSS_SVM          L2-loss support vector machines (primal)
-    L1LOSS_SVM_DUAL     L1-loss support vector machines (dual)  
-    MCSVM_CS multi-class support vector machines by Crammer and Singer        
+    L2_LR                L2-regularized logistic regression
+    L2_L2LOSS_SVC_DUAL   L2-regularized L2-loss support vector classification (dual) 
+    L2_L2LOSS_SVC        L2-regularized L2-loss support vector classification (primal)
+    L2_L1LOSS_SVC_DUAL   L2-regularized L1-loss support vector classification (dual)  
+    MCSVM_CS multi-class support vector classification by Crammer and Singer        
+    L1_L2LOSS_SVC        L1-regularized L2-loss support vector classification
+    L1_LR                L1-regularized logistic regression
 
     C is the cost of constraints violation. 
     eps is the stopping criterion.