svm

问题 How do I create a database of SIFT descriptors (of images)? My intention is to implement a supervisioned training set on Support Vector Machine. 回答1: Which kind of images do you need? If you don`t care, you can just download some public computer vision dataset like http://lear.inrialpes.fr/~jegou/data.php#holidays which offers both images and already computed SIFTs from its regions. Or try other datasets, for instance, from http://www.cvpapers.com/datasets.html Other possibility is just to

问题 I am building a SVM model using RSofia package in R. The following is the code I tried on a sample data: library(RSofia) x1 <- as.factor(sample(c("a","b","c"),1000,replace = TRUE)) x2 <- as.factor(sample(c("e","f","g"),1000,replace = TRUE)) y <- as.factor(sample(c(0,1),1000,replace = TRUE)) train <- data.frame(x1=x1,x2=x2,y=y) lambda <- 0.01 iterations <- 1e+07 learner_type <- 'pegasos' eta_type <- 'pegasos' loop_type <- 'rank' rank_step_probability <- 0.5 sofiaModel <- sofia(y ~ ., data

问题 I have a data frame trainData which contains 198 rows and looks like Matchup Win HomeID AwayID A_TWPCT A_WST6 A_SEED B_TWPCT B_WST6 B_SEED 1 2010_1115_1457 1 1115 1457 0.531 5 16 0.567 4 16 2 2010_1124_1358 1 1124 1358 0.774 5 3 0.75 5 14 ... The testData is similar. In order to use SVM, I have to change the response variable Win to a factor . I tried the below: trainDataSVM <- data.frame(Win=as.factor(trainData$Win), A_WST6=trainData$A_WST6, A_SEED=trainData$A_SEED, B_WST6=trainData$B_WST6,

问题 I've recently been using Blender to render 3D models of objects and training an SVM to recognize pictures of object taken from some perspective. To train said SVMs I need to use "sklearn", which comes by default with Anaconda. Long story short, I want Blender (which runs Python 3.4.2) to use the packages and modules present in my anaconda installation (which runs Python 3.4.3). I've tried a variety of things following this website: https://www.blender.org/api/blender_python_api_2_60_1/info

问题 I am using opencv3.0,My IDE is pycharm I have two lists one list of training_set and one list of trainig_labels. training_set is a list of lists like [array([119, 122, 91, ..., 185, 80, 255], dtype=uint8), array([112, 106, 120, ..., 121, 138, 255], dtype=uint8), ....... ] training_labels is list of labels for each list in training_set.training_labels looks like [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] But when i try to pass this to svm.train()

问题 I found an implementation in http://code.google.com/p/cascadesvm/. However, there are no specifications about that. Has anyone tried that? Or where could I find an alternative implementation of SVM on Hadoop? Thanks a lot~ 回答1: Looks like someone did this within the Mahout project, not sure if it's been merged into trunk, but this looks like a good place to start: https://issues.apache.org/jira/browse/MAHOUT-232 回答2: You can check it out https://code.google.com/p/cascadesvm/ The training part

问题 I have read through a lot of papers and understand the basic concept of a support vector machine at a very high level. You give it a training input vector which has a set of features and bases on how the "optimization function" evaluates this input vector lets call it x, (lets say we're talking about text classification), the text associated with the input vector x is classified into one of two pre-defined classes, this is only in the case of binary classification. So my first question is

问题 I'd like to pass a sparse precomputed Gram matrix to sklearn.svm.SVC.fit. Here's some working code: import numpy as np from sklearn import svm X = np.array([[0, 0], [1, 1]]) y = [0, 1] clf = svm.SVC(kernel='precomputed') gram = np.dot(X, X.T) clf.fit(gram, y) But if I have: from scipy.sparse import csr_matrix sparse_gram = csr_matrix(gram) clf.fit(sparse_gram, y) I get: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python2.7/dist-packages/sklearn

问题 The opencv SVM implementation takes a parameter labeled as "SVM type" which must be used in the CVSVMParams structure used in training the SVM. All the explanation I can find is: // SVM type enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 }; Anyone know what these different values represent? 回答1: In general: Classification SVM Type 1 (also known as C-SVM classification) Classification SVM Type 2 (also known as nu-SVM classification) Regression SVM Type 1 (also known as

问题 The opencv SVM implementation takes a parameter labeled as "SVM type" which must be used in the CVSVMParams structure used in training the SVM. All the explanation I can find is: // SVM type enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 }; Anyone know what these different values represent? 回答1: In general: Classification SVM Type 1 (also known as C-SVM classification) Classification SVM Type 2 (also known as nu-SVM classification) Regression SVM Type 1 (also known as