matlab machine learning toolbox

When learning machine learning, we basically use MATLAB and python to write algorithms and perform tests; Recently, thanks to the use of opencv to write homework, we have taken a look at the Machine Learning Library (MLL) of openc

Http://archive.ics.uci.edu/ml/ The database is a machine learning database proposed by the University of California at the University of Virginia (universityofcaliforniairvine). There are currently 187 datasets in this database, and the number of these databases is increasing, UCI dataset is a common standard test dataset. The "multiplefeatures" database on UCI is a handwritten digital recognition problem

regenerate a decision tree with each decision tree, which describes the method of pickle serialization to store an already generated decision tree.The basic usage of the Pickle module is as follows.Use the Pickle module to store decision Trees:def storeTree(inputTree,filename): import pickle fw = open(filename,‘w‘) pickle.dump(inputTree,fw) fw.close()def grabTree(filename): import pickle fr = open(filename) return pickle.load(fr)The storage format is as follows:Implementati

The first contact with SVM was still four years ago. At that time, it was used for handwritten digital recognition. Based on some books and literature, MATLAB was used to extract the PCA + SVMCode, The recognition rate is normal, 90 is not on, sorry to say hello to people. Most importantly, when I attended an interview, I was asked that Shenma is a support vector and I couldn't answer it. After being a graduate student, I learned this classic story re

, which isparameterized by its second argument C. Here Myfun isA MATLAB file function such asfunction [F,g]=Myfun (x,c) F= C*x (1)^2+2*x (1) *x (2) + x (2)^2; %function G= [2*c*x (1) +2*x (2) %Gradient2*x (1) +2*x (2)]; To optimize fora specific value of C, first assign the value to C. Then create a one-argument anonymous function, that captures, the value of C and calls Myfun with the arguments. Finally, pass. Thisanonymous function to

: Libsvm-farutogui version Introduction and useHttp://www.matlabsky.com/thread-18606-1-1.htmlMATLAB Technology Forum Data Mining public Class (eight) theory and application of support vector machineHttp://www.matlabsky.com/thread-33968-1-1.html============================================MATLAB support Vector MachinePlateHttp://www.matlabsky.com/forum-26-1.htmlThe dissected thing about SVM [Faruto long-term update finishing]Http://www.matlabsky.com/thr

Tools used: NumPy and MatplotlibNumPy is the most basic Python programming library in the book. In addition to providing some advanced mathematical algorithms, it also has a very efficient vector and matrix operations function. These are particularly important for computational tasks for machine learning. Because both the characteristics of the data, or the batch design of parameters, are inseparable from t

Tags: category Pat consumer fast Clustering gravity technology Clust parametersAn overview of density-based clustering algorithms recently, a density-based clustering algorithm in science, "clustering by fast search and find of density peaks" attracted attention (in my blog "The Machine Learning algorithm--the base The clustering algorithm for density peaks is also described in Chinese). So I want to under

same. In addition, it is necessary to feature scale (Features scaling) features before running the gradient descent algorithm.Some options beyond the gradient descent algorithm:In addition to the gradient descent algorithm, there are algorithms that are often used to minimize the cost function, which are more complex and excellent, and typically do not require manual selection of learning rates, and are often faster than gradient descent algorithms.

normal equation.The normal equation is to find the parameter that minimizes the cost function by solving the following equation:Assuming our training feature matrix is x (which contains x0=1) and our training set results in Vector y, the normal equation is used to solve the vector:Callout: T denotes transpose of matrix X, 1 indicates inverse of matrix XWe use the price forecast problem data:The data includes four features (excluding X0), we add x0=1, and we use the normal equation method to sol

/bpr.htmlBibliographies on Neural Networkshttp://liinwww.ira.uka.de/bibliography/Neural/Intelligent Motion Control with an Artificial cerebellumHttp://www.q12.org/phd.htmlKernel Machineshttp://www.kernel-machines.org/Some Neural Networks Organizationshttp://www.ieee.org/nnc/http://www.inns.org/Neural Network Modeling in VisionHttp://www.rybak-et-al.net/nisms.htmlNeural Networks and machine learninghttp://learning.cs.toronto.edu/Neural application soft

category by two, and get N classifiers.When testing is required, input the data into each classifier, selecting one of the largest probabilities as the output.SummaryLogistic regression is built on the basis of linear regression. The model is: the probability that the output is 1 through the sigmoid function. The application should conform to the Bernoulli distribution in the output.The gradient descent algorithm is also useful, and there are some more efficient algorithms. At first, you can us

-2.7315Bicentset = -3.3824 -2.9473 0.0825 2.9480 2.8029 -2.7315 0 0The error after the 1th cluster is divided is: 395.669052The error after the 2nd cluster is divided is: 149.954305The error after the 3rd cluster is divided is: 393.431098Bestclustertospilt = 2Bestcentset =2.6265 3.1087-2.4615 2.7874Bicentset = -3.3824 -2.9473 2.6265 3.1087 2.8029 -2.7315 -2.4615 2.7874Eventually When using the binary K-means algorithm for clustering, the re

any number of elements. However, all elements must be of the same type, for example, a vector cannot contain both numbers and text.integer numeric character logical NULL NACombine functionVectors in • R have an inherent order, so their data can be accessed by counting the sequence numbers of each element in the vector, starting from 1• Factor• A factor is a special case of a vector that is uniquely used to identify a nominal attribute• Why not character the character vector?• Convert a characte

cost function least.The algorithm is:After derivation, get:Note: Although the resulting gradient descent algorithm appears to be the same as the gradient descent algorithm for linear regression, the hypothetical function here differs from the linear regression, so it is actually different. In addition, it is still necessary to perform feature scaling before applying the gradient descent algorithm.In addition, there are some alternatives to the gradient descent algorithm:In addition to the gradi

what is linear regression. The so-called linear regression (taking a single variable as an example) is to give you a bunch of points, and you need to find a straight line from this pile of points. Figure below This screenshot is from Andrew Ng's What you can do when you find this line. Let's say we find A and b that represent the line, then the line expression is y = a + b*x, so when a new x is present, we can know Y. Andrew ng First Class said, what is mach