How should I approach a situtation when I try to apply some ML algorithm (classification, to be more specific, SVM in particular) over some high dimensional input, and the results I get are not quite
Which approach would you suggest for automatically classifying type found in images? The samples are likely large, with black text on a white background.
I\'m trying to build an app to detect images which are advertisements from the webpages. Once I detect those I`ll not be allowing those to be displayed on the client side.
I am now using libsvm for support vector machine classifier with Gaussian kernal. In its website, it provides a python script grid.py to select the best C and gamma.
I am writing a fairly complicated machine learning program for my thesis in computer vision. It\'s working fairly well, but I need to keep trying out new things out and adding new functionality. This
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I\'m trying to read an image from an electrocardiography and detect each one of the main waves in it (P wave, QRS complex and T wave). I can read the image and get a vector (like (4.2; 4.4; 4.9; 4.7;
The need for having part of the training set used as verification data is straightforward, but I am not really clear on how and at what stage of the training should it be incoperated?
Backpropagation calculates dW (weight delta) per weight per pattern, so 开发者_JS百科it\'s straightforward how to modify weights when doing stochastic training. How do I use it for batch training, tho
I want to use Gaussian mixture modelsfor data clustering ( using an expectation maximization (EM) algorithm, which assigns posterior probabilities to each component density with respect to each observ
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