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]download flash player 10 for freeQ: Feature extraction classifier I was given a dataset and was asked to train a classifier, which takes a video as an input and predicts whether it belongs to the emotion class or not, without having emotion labels for training. The categories i was given are: neutral, happy, sad, angry, scared, surprised and disgusted. The input features are just the mean values and standard deviations. I was looking for some open source implementations (https://github.com/… or… etc), which would help me to train the classifier. A: If you want to avoid having emotion labels, you should start with a semi-supervised learning approach. If I understand you correctly, the workflow is as follows: Training -> Validation Input -> Feature extraction Train a linear classifier Validate on the validation set General algorithm: Train the model on the labeled set (train data) Test it on the unlabeled set (validation data) Use some technique to get the “best” model weights (if no forced model selection strategy is used) When learning your model, you could use a few metrics, such as margin, for accuracy, mean squared error for regression (makes sense for your problem) or regression for binary classification (much less sense, but you could use them in a validation step). If you want to try this approach, you can find a simple example here. In step 6, you should try “bagging” since you will have a set of models that can be used for a prediction. In step 6, you can also try feature selection, but this approach may get messy very fast. For instance, in the feature selection process, you may build several models, remove redundant features and try to avoid “catastrophic” training failures (e.g., if using linear models), but it all depends on what is contained in your dataset. If you have only a few features, you can also try computing Principal Components Analysis and follow this approach: Compute the Principal Components of your training set Perform linear regression on the Principal Components. Perform a stepwise regression on your training set. In step 4, you may use cross-validation to choose the “best” model hyper-parameters.

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