svm

问题 I am a beginner to SVM which i have successfully implemented one-class classification.Now i want to know about multi-class classification which am very much confused about. I went through How to do multi class classification using Support Vector Machines (SVM) which i want the exact same output but the link does not have a specific example using windows.If anyone can help me out with an example in windows for both “ONE-AGAINST-ONE”,”ONE-AGAINST-ALL” methods of multi-class classification

问题 My goal here is to classify between SUVs and sedans using SVMs and HOG features. First I read 86 training images, calculate the HOG features for each of them, and put them in a training Mat that is of size 86xdescriptorSize called HOGFeat_train. Mat HOGFeat_train(num_train_images, derSize, CV_32FC1); //86xdescriptor size training Mat for (int file_count = 1; file_count < (num_train_images + 1); file_count++) { ss << name << file_count << type; //'Vehicle_1.jpg' ... 'Vehicle_2.jpg' ... etc ...

问题 I am trying to predict boston housingdata by using SVR and I am using the following code but getting some error. # -*- coding: utf-8 -*- import sys import pandas as pd columns=['ID','crim','zn','indus','chas','nox','rm','age','dis','rad','tax','ptratio','black','lstat','medv'] dataset_train=pd.read_csv('train.csv')#,names=columns) train_y=pd.DataFrame(dataset_train.medv) dataset_train=dataset_train.drop('medv',axis=1) columns_test=['ID','crim','zn','indus','chas','nox','rm','age','dis','rad',

问题 I need a svm classifier of python with huber loss function. But its default loss function is hinge loss. Do you know how can I assign loss function to python svm? svc = svm.SVC(kernel='linear', C=1, gamma=1).fit(data, label) 回答1: There is really no such thing as "SVM with huber loss", as SVM is literally a linear (or kernelized) model trained with hinge loss. If you change the loss - it stops being SVM. Consequently libraries do not have a loss parameter, as changing it does not apply to the

问题 Would a 100-by-3 dimensional feature matrix be mapped into a 100 dimensional or into a infinite dimensional feature space, if the mapping would not be bypassed by the Gaussian RBF Kernel? Following this reasoning (The RBF kernel of Support Vector Machine) I would tend to say the feature matrix would be mapped to a inifinite dimensional feature space. Here a summary of the content: Given a m-by-n feature matrix X . Each n-dimensional instance x of X is used to define a n-dimensional normal

问题 Well, I am making a sentiment analysis classifier and I have three classes/labels, positive, neutral and negative. The Shape of my training data is (14640, 15), where negative 9178 neutral 3099 positive 2363 I have pre-processed the data to make it standardized and applied the bag-of-words word vectorization technique to the text of twitter for making it feedable to the model, whose size is then (14640, 1000). As the Y, means the label is in the text form so, I applied LabelEncoder so that I

问题 I am working on a project in my school to detect how many students are in the classroom. Like in this picture. I have been trying to use Haar Cascade in opencv for face detection to detect people, but the result is very bad. Like this: I took thousands of pictures in classroom, and cropped the picture with people manually. There are about 4000 positive samples and 12000 negative samples. I was wondering what did I do wrong? When I crop the image, should I only crop only head like this? Or

问题 I'm having trouble getting my SVM to predict 0's and 1's where I would expect it to. It seems that after I train it and give it more data, it always wants to predict a 1 or a 0, but it will predict all 1's or all 0's, and never a mix of the two. I'm wondering if one of you could tell me what I'm doing wrong. I've searched for "svm always predicting same value" and similar problems, and it looks like this is pretty common for those of us new to machine learning. I'm afraid though that I don't

问题 My aim is to classify an impulsive audio signal as whether it is a gunshot or not a gunshot. I am trying to detect the gunshot event in MATLAB using svmtrain and svmclassify functions. To evaluate the accuracy of classification, the balloon burst and clapping signal are used as different classes. While accuracy of differnetiating between gunshot and either of the two classes is good, the differentiation between gunshot and combined baloon+clap signal is poor. Please guide how may I use SVM to

问题 I am currently working on a support vector machine (SVM) project. The version of SVM that I am working on is Linear SVM in Primal Form and I am having hard time understanding where to start. In general, I think I understand the theory; basically I need to minimize norm of w under certain constraint. And the Lagrangian function will be my objective function to be minimized (after Lagrange multiplier is applied). The things that I don't understand is that I was told from my professor that we