popular machine learning algorithms

-cutting field involving mathematics, automation, computer science, applied psychology, biology, and neurophysiology. The virtuous interaction brought by this interdisciplinary integration undoubtedly promotes the development and prosperity of various disciplines, including machine learning.The content of this book is very rich, the author has not had the breadth and depth, introduced the current machine

Original: http://www.52ml.net/15063.htmlHow to choose a machine learning algorithmMay 7, 2014 machine learning smallroof How does you know the learning algorithm to choose for your classification problem? Of course, if you really care about accuracy, your best bet was to te

Machine Learning notes of the Dragon Star program 　Preface In recent weeks, I spent some time learning the machine learning course of the Dragon Star program for the next summer vacation. For more information, see the appendix. This course chooses to talk about the basic mod

Integrated Learning AlgorithmsIn statistics and machine learning, the integrated approach uses a variety of learning algorithms to obtain better predictive performance than a single learning algorithm from any composition. the eff

various machine learning fields, such as minimizing the maximum loss, sequential decision, and parameter estimation. Naive Bayes is one of them. This is also a type of algorithm.8. Bayesian Network: A theory supported by reasoning and planning theories.9. Sequence Analysis Method: Analyzes the learning of a sequence. A language is a sequence of words, so it is a

take an average of this evaluation mode.It is a useful algorithm to use the F-score algorithm to evaluate both precision and recall rates . The PR of the molecule determines that the precision ratio (P) and recall (R) must be large at the same time to ensure that the F score values are larger. If the precision ratio or recall rate is very low, close to 0, the direct result of the PR value is very low, approaching 0, that is, F score is also very low.At this point we compare three

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

with code cleanliness, readability, and speed in mindPattern Recognition ToolboxPattern Recognition Toolkit in-matlab, fully object-orientedData analysis/Data visualizationMatlab_gbl-MATLAB package for image processinggamic-Image algorithm Pure matlab efficient implementation, to MATLABBGL's Mex function is a supplementsecond,. NET Computer Visionopencvdotnet-Wrapper to enable. NET programs to use OPENCV codeEMGU CV-Cross-platform wrappers that can be compiled on Windows, Linus, Mac OS X, iOS a

algorithms, and complex. I try to divide a class, mainly hierarchical networks, time-delay neural networks, coupled neural networks, self-organizing neural networks, recurrent neural networks (and time-delay neural network somewhat like, but in the continuous and discrete amount of slightly different, continuous use analog circuit implementation), Radial basis function network (this is actually a regularization network, The General RBF Network is the

() 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