matlab machine learning toolbox

MATLAB machine learning did not see what tutorial, only a series of functions, had to record:Matlab Each machine learning method is implemented in many ways, and can be advanced configuration (such as the training decision tree when the various parameters set), here due to s

Machine Learning-Overview of common matlab programming commands -- Summary from ng-ml-class octave/MATLAB tutorial CourseraA. basic operations and moving data around1 in command line mode, you can use Shift + press enter to append the next line to output 2 length command to apply to the matrix, and return a higher one-

Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job four q13-20 MATLAB implementation. The previous code was implemented through C + +, but found that C + + implementation of the code is too cumbersome, the job also to change the

Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job four q13-20 MATLAB implementation.Once the code is implemented through C + +. However, it is too cumbersome to discover that C + + implements this code. This job also need to cha

The course covers technology:Gradient descent, linear regression, supervised/unsupervised learning, classification/logistic regression, regularization, neural network, gradient test/numerical calculation, model selection/diagnosis, learning curve, evaluation metric, SVM, K-means clustering, PCA, Map Reduce Data Parallelism, etc...The course covers applications:Message classification, tumor diagnosis, handw

MATLAB toolbox for signal processing and Pattern RecognitionIf You Do wavelet, ICA, PCA, SVM, kernel and other methods, we hope the following tools will help you. Signal Processing (top)Filter Design with Motorola dsp56kHttp://www.ee.ryerson.ca: 8080 /~ Mzeytin/DFP/index.html Change Detection and adaptive filtering toolbox Http://www.sigmoid.se/ Signal Processing

);4. Simulation TestT_sim = Sim (net,p_test);5. Inversion of dataT_sim = Mapminmax (' reverse ', t_sim,ps_output);V. Performance evaluation1. Absolute error errorsError = ABS (t_sim-t_test)./t_test;2. Decision Factor r^2R2 = (n * SUM (t_sim. * t_test)-sum (t_sim) * SUM (t_test)) ^2/((N * SUM ((T_sim). ^2)-(SUM (T_sim)) ^2) * (n * SUM (t_test) . ^2)-(SUM (t_test)) ^2));3. Comparison of resultsresult = [t_test ' T_sim ' ERROR ']result = 28.1600 31.1291 0.1054 52.6500 53.0643 0.0079

Https://www.coursera.org/learn/machine-learning/exam/dbM1J/octave-matlab-tutorial Octave Tutorial 5 questions 1.Suppose I first execute the following Octave commands: A = [1 2; 3 4; 5 6]; B = [1 2 3; 4 5 6]; Which of the following is then valid Octave commands? Check all, apply and assume all options is written in an Octave command.

% condition for training set no mis-classification points forI=1: Kif(Y (i) * (dot (w,x (i,:)) +b) 0% determine if the classification is incorrectW (1) =w (1) +r*y (i) *x (i,1); % corresponding algorithm the third step W (2) =w (2) +r*y (i) *x (i,2); b=b+r*y (i); W=[w (1), W (2)]; WR=[Wr;w]; BR=[Br,b]; T=t+1;EndEnd forI=1: K Con1 (i)= (Y (i) * (dot (w,x (i,:)) +b)); % if all points are classified correctly, then all elements in Con1 are greater than 0 end con= (All (Con1 (:) >0)) Endxt=-2:0.1

(costfunction, Initial_nn_params, options);% Obtain Theta1 and Theta2 back from nn_paramstheta1 = Reshape (Nn_params (1:hidden_layer_size * (input_layer_size + 1)), ... . Hidden_layer_size, (InpuT_layer_size + 1)); Theta2 = Reshape (Nn_params ((1 + (Hidden_layer_size * (input_layer_size + 1)): End), ... num_labels, (hidden_layer_size + 1) ); fprintf (' program paused. Press ENTER to continue.\n ');p ause;%% ================= part 9:visualize Weights =================% You can now "Visualiz E "W

1 clear All;2 CLC;3%%4%algorithm5% Input: training data Set t ={(x1,y1), (X2,y2), ..., (Xn,yn)}; learning rate η6% output: w,b; Perceptron model f (x) = sign (w*x+b)7%(1) Select the initial value w0,b08%(2) Select data in the training set (Xi,yi)9%(3) if Yi (w*xi+b) 0Ten% W = w+η*yi*XI One% B = C +Ηyi A%(4) Go to (2) until there are no mis-classification points in the training set -%% - the%Initialize -X = [3 3 1;4 3 1;1 1-1];%Training Set -[SN,FN] =