statistics and machine learning toolbox matlab

% 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

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] =

(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

Huadian North Wind BlowsKey laboratory of cognitive computing and application, Tianjin UniversityModification Date: 2015/8/15 Decision tree is a very simple machine learning classification algorithm. The decision tree idea comes from the human decision-making process. For the simplest example, when humans find it raining, they tend to have an easterly wind and then darken the sky. Corresponding to the decis

The relationship between probability statistics and machine learningProbability problem is known as the whole case of the decision sample (whole push individual)Statistical problem is reverse engineering of probability problem (individual pushing whole)In machine learning supervised

perform a multiple regression of over Million variables? The idea is ludicrous. That's because training VGG-16 is not multiple regression?—? it's machine learning. I would also like to point out that one of the differences between deep learning networks and traditional statistical models is their scale problems. The scale of the deep neural network is

. So, why is this so? The reason is simple: there are highly correlated variables in the data (up to 0.987 of the x1,x2 correlation), and the two variables are so similar, like two parallel vectors, that is, they're collinear . Popular, because two software is too similar, so that cannot judge who can contribute greater user satisfaction, the two 10:0 open, 5:5 Open, 0:10 open almost no difference. As can be seen from the above results, the standard error of β1 reached 2.3947 and β2 reached 2.4

Probability statistics The relationship between probability statistics and machine learning Statistic Amount Expect Variance and covariance Important theorems and inequalities Jensen Inequalities Chebyshev on the snow Man's inequality Large number theorem T

　　From a statistical point of view, most of the methods of machine learning are statistical classification and regression method to the field of engineering extension.The term "regression" (Regression) was the origin of the British scientist Francis Galton (1822-1911) in a 1886 paper [1] to study the relationship between height and parental height of a child. After observing 1087 couples, the adult son was