Machine learning, best technique

I am new to machine learning. I am familiar with 开发者_如何学编程SVM , Neural networks and GA. I'd like to know the best technique to learn for classifying pictures and audio. SVM does a decent job but takes a lot of time. Anyone know a faster and better one? Also I'd like to know the fastest library for SVM.

Your question is a good one, and has to do with the state of the art of classification algorithms, as you say, the election of the classifier depends on your data, in the case of images, I can tell you that there is one method called Ada-Boost, read this and this to know more about it, in the other hand, you can find lots of people are doing some researh, for example in Gender Classification of Faces Using Adaboost [Rodrigo Verschae,Javier Ruiz-del-Solar and Mauricio Correa] they say:

"Adaboost-mLBP outperforms all other Adaboost-based methods, as well as baseline methods (SVM, PCA and PCA+SVM)" Take a look at it.

If your main concern is speed, you should probably take a look at VW and generally at stochastic gradient descent based algorithms for training SVMs.

if the number of features is large in comparison to the number of the trainning examples then you should go for logistic regression or SVM without kernel

if the number of features is small and the number of training examples is intermediate then you should use SVN with gaussian kernel

is the number of features is small and the number of training examples is large use logistic regression or SVM without kernels .

that's according to the stanford ML-class .

For such task you may need to extract features first. Only after that classification is feasible.

I think feature extraction and selection is important.

For image classification, there are a lot of features such as raw pixels, SIFT feature, color, texture,etc. It would be better choose some suitable for your task.

I'm not familiar with audio classication, but there may be some specturm features, like the fourier transform of the signal, MFCC.

The methods used to classify is also important. Besides the methods in the question, KNN is a reasonable choice, too.

Actually, using what feature and method is closely related to the task.

The method mostly depends on problem at hand. There is no method that is always the fastest for any problem. Having said that, you should also keep in mind that once you choose an algorithm for speed, you will start compromising on the accuracy.

For example- since your trying to classify images, there might a lot of features compared to the number of training samples at hand. In such cases, if you go for SVM with kernels, you could end up over fitting with the variance being too high. So you would want to choose a method that has a high bias and low variance. Using logistic regression or linear SVM are some ways to do it.

You could also use different types of regularizations or techniques such as SVD to remove the features that do not contribute much to your output prediction and have only the most important ones. In other words, choose the features that have little or no correlation between them. Once you do this, you would be able to speed yup your SVM algorithms without sacrificing the accuracy.

Hope it helps.

there are some good techniques in learning machines such as, boosting and adaboost. One method of classification is the boosting method. This method will iteratively manipulate data which will then be classified by a particular base classifier on each iteration, which in turn will build a classification model. Boosting uses weighting of each data in each iteration where its weight value will change according to the difficulty level of the data to be classified. While the method adaBoost is one ensamble technique by using loss function exponential function to improve the accuracy of the prediction made.

I think your question is very open ended, and "best classifier for images" will largely depend on the type of image you want to classify. But in general, I suggest you study convulutional neural networks ( CNN ) and transfer learning, currently these are the state of the art techniques for the problem.

check out pre-trained models of cnn based neural networks from pytorch or tensorflow

Related to images I suggest you also study pre-processing of images, pre-processing techniques are very important to highlight some feature of the image and improve the generalization of the classifier.