From c67b8bc4e02646c8ce781632dab4f1115c3e07a2 Mon Sep 17 00:00:00 2001 From: Ali Shirvani Date: Wed, 17 Jun 2015 14:44:45 +0430 Subject: [PATCH] make it little cleaner --- linear-classify.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/linear-classify.md b/linear-classify.md index 603defd7..90cf56d1 100644 --- a/linear-classify.md +++ b/linear-classify.md @@ -115,7 +115,7 @@ For example, going back to the example image of a cat and its scores for the cla There are several ways to define the details of the loss function. As a first example we will first develop a commonly used loss called the **Multiclass Support Vector Machine** (SVM) loss. The SVM loss is set up so that the SVM "wants" the correct class for each image to a have a score higher than the incorrect classes by some fixed margin \\(\Delta\\). Notice that it's sometimes helpful to anthropomorphise the loss functions as we did above: The SVM "wants" a certain outcome in the sense that the outcome would yield a lower loss (which is good). -Let's now get more precise. Recall that for the i-th example we are given the pixels \\( x\_i \\) and the label \\( y\_i \\) that specifies the index of the correct class. The score function takes the pixels and computes the vector \\( f(x\_i, W) \\) of class scores. For example, the score for the j-th class is the j-th element: \\( f(x\_i, W)\_j \\). The Multiclass SVM loss for the i-th example is then formalized as follows: +Let's now get more precise. Recall that for the i-th example we are given the pixels of image \\( x\_i \\) and the label \\( y\_i \\) that specifies the index of the correct class. The score function takes the pixels and computes the vector \\( f(x\_i, W) \\) of class scores. For example, the score for the j-th class is the j-th element: \\( f(x\_i, W)\_j \\). The Multiclass SVM loss for the i-th example is then formalized as follows: $$ L\_i = \sum\_{j\neq y\_i} \max(0, f(x\_i, W)\_j - f(x\_i, W)\_{y\_i} + \Delta)