I am developing an OCR using SVM in opencv C++. SVM used is (one vs one multi-class) linear SVM. Opencv svm (multi-class)doesn't give probability estimates for each class that was used in time of training. So i tried my luck with libsvm Multi-class classification (and probability output) via error-correcting codes. It gave me the probability estimates for each of the class, now I want to use the training model file in opencv C++. I get an error. Now my problem is how to use the training model in opencv, if not possible how to get probability estimates for each of the class using (one vs one multi-class) linear svm ?
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In TensorFlow library, what does the tf.estimator.LinearClassifier class do in linear regression models? (In other words, what is it used for?)
Linear Classifier is nothing but Logistic Regression.
According to Tensorflow documentation, tf.estimator.LinearClassifier is used to
Train a linear model to classify instances into one of multiple
possible classes. When number of possible classes is 2, this is binary
classification
Linear regression predicts a value while the linear classifier predicts a class. Classification aims at predicting the probability of each class given a set of inputs.
For implementation of tf.estimator.LinearClassifier, please follow this tutorial by guru99.
To know about the linear classifiers, read this article.
I'm following a TensorFlow example that takes a bunch of features (real estate related) and "expensive" (ie house price) as the binary target.
I was wondering if the target could take more than just a 0 or 1. Let's say, 0 (not expensive), 1 (expensive), 3 (very expensive).
I don't think this is possible as the logistic regression model has asymptotes nearing 0 and 1.
This might be a stupid question, but I'm totally new to ML.
I think I found the answer myself. From Wikipedia:
First, the conditional distribution y|x is a Bernoulli distribution rather than a Gaussian distribution, because the dependent variable is binary. Second, the predicted values are probabilities and are therefore restricted to (0,1) through the logistic distribution function because logistic regression predicts the probability of particular outcomes.
Logistic Regression is defined for binary classification tasks.(For more details, please logistic_regression. For multi-class classification problems, you can use Softmax Classification algorithm. Following tutorials shows how to write a Softmax Classifier in Tensorflow Library.
Softmax_Regression in Tensorflow
However, your data set is linearly non-separable (most of the time this is the case in real-world datasets) you have to use an algorithm which can handle nonlinear decision boundaries. Algorithm such as Neural Network or SVM with Kernels would be a good choice. Following IPython notebook shows how to create a simple Neural Network in Tensorflow.
Neural Network in Tensorflow
Good Luck!
I am new to opencv. I want to use SVM in opencv. My question is can I train the classifier once and save all vectors. So in my main program I just need to import these vectors and do the classification. I read the SVM document and I only find get_support_vector function to get all vectors but I didn't find set_support_vector function. Does anybody have idea how to re-use a trained classifier? Thanks.
I am trying to train a SVM Classifier and use the same for Human Detection. I have around 3000 positive and 3000 negative samples to be used for training. HOG Features are used for feature Extraction (I am not making use of any OpenCV Code for HOG, rather histograms are calculated using separate piece of C++ Code). But for training and prediction I am relied on OpenCV SVM.train() and SVM.predict() classes.
When I run the Code, I get the SVM Classifier dumped (using "SVM.save"), but when i use the same for predicting the Images (Here i am using the same Images which were used for training) i see almost all the Negative Images are falsely-classified as positive.
I even tried varying the "CvSVMParams params", but it did not yield me any result.
The params used were :
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::RBF;
params.term_crit = cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON);
Any suggestions/advice on this would be of great help.
Thanks in Advance.
I would like to load a model I trained before and then update this model with new training data. But I found this task hard to accomplish.
I have learnt from Weka Wiki that
Classifiers implementing the weka.classifiers.UpdateableClassifier interface can be trained incrementally.
However, the regression model I trained is using weka.classifiers.functions.MultilayerPerceptron classifier which does not implement UpdateableClassifier.
Then I checked the Weka API and it turns out that no regression classifier implements UpdateableClassifier.
How can I train a regression model in Weka, and then update the model later with new training data after loading the model?
I have some data mining experience in Weka as well as in scikit-learn and r and updateble regression models do not exist in weka and scikit-learn as far as I know. Some R libraries however do support updating regression models (take a look at this linear regression model for example: http://stat.ethz.ch/R-manual/R-devel/library/stats/html/update.html), so if you are free to switching data mining tool this might help you out.
If you need to stick to Weka than I'm afraid that you would probably need to implement such a model yourself, but since I'm not a complete Weka expert please check with the guys at weka list (http://weka.wikispaces.com/Weka+Mailing+List).
The SGD classifier implementation in Weka supports multiple loss functions. Among them are two loss functions that are meant for linear regression, viz. Epsilon insensitive, and Huber loss functions.
Therefore one can use a linear regression trained with SGD as long as either of these two loss functions are used to minimize training error.