octave machine learning

Because there is a very detailed online blog, so this section will not write their own, write can not write others so good and thorough.jerrylead Support Vector Machine series:Support Vector Machine (i): http://www.cnblogs.com/jerrylead/archive/2011/03/13/1982639.htmlSupport Vector Machine (ii): http://www.cnblogs.com/jerrylead/archive/2011/03/13/1982684.htmlSupp

(Digits.data, - Digits.target, intest_size=0.25, -Random_state=33) to + " " - 3 recognition of digital images using support vector machine classification model the " " * #standardize training data and test data $SS =Standardscaler ()Panax NotoginsengX_train =ss.fit_transform (X_train) -X_test =ss.fit_transform (x_test) the + #Support Vector machine classifier for initializing linear hypothesis ALsvc =lin

() function is used to convert the 32x32 binary image to the 1x1024 vector and the loadimages () function to load the image.Four Test results and methodsThe number of support vectors, the error rate of training set and the error rate of test set are tested with the testdigits () function.After 4 iterations are obtained:Five Kernel functionThe kernel function is the core algorithm of SMV, and for a sample that is linearly non-divided, the original input space can be linearly divided into a new k

Machine Learning is to study how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their own performance. It is the core of artificial intelligence and the fundamental way to make computers intelligent. It is applied in various fields of artificial intel

1. Overview:The first step in learning a subject is to understand what this knowledge is and what it can be used for.This article lists some of the more well-written articles in the process of learning machine learning and the initial impressions of machines learning after r

1, Xiao Wei's practice road Http://blog.csdn.net/xiaowei_cqu 2, Morning Chenyusi far (Shi Yuhua Beihang University) Http://blog.csdn.net/chenyusiyuan 3, Rachel Zhang (Zhang Ruiqing) 's blog Http://blog.csdn.net/abcjennifer 4. ZOUXY09 (Shaoyi) http://blog.csdn.net/zouxy09 (deep learning, image segmentation, Kinect development Learning, compression sensing) 5, Love CVPR HTTP://BLOG.CSDN.NET/ICVPR 6, focus on

Hello everyone, I am mac Jiang. See everyone's support for my blog, very touched. Today I am sharing my handwritten notes while learning the cornerstone of machine learning. When I was studying, I wrote down something that I thought was important, one for the sake of deepening the impression, and the other for the later review.Online

1. Integrated Learning OverviewIntegrated learning algorithm can be said to be the most popular machine learning algorithms, participated in the Kaggle contest students should have a taste of the powerful integration algorithm. The integration algorithm itself is not a separate mac