# Tag: Support Vector Machines

## Factorization Machines for Movie Recommendations

In the last series of posts we have looked at how to recommend movies to users based on the historical ratings. The two most promising approaches were Collaborative Filtering and Matrix Factorization. Both these approaches learns the user-movie preferences only from the ratings matrix. Recall that in the first post of the series, we had started with an approach known as the Content Based Recommendation, where we created a regression […]

## Using Word Vectors in Multi-Class Text Classification

Earlier we have seen how instead of representing words in a text document as isolated features (or as N-grams), we can encode them into multidimensional vectors where each dimension of the vector represents some kind semantic or relational similarity with other words in the corpus. Machine Learning problems such as classification or clustering, requires documents to be represented as a document-feature matrix (with TF or TF-IDF weighting), thus we need some […]

## Kernels and Support Vector Machines

Given a supervised classification problem with the set of N training examples along with the class labels , i.e. , we need to build a model to predict the class label for an unseen example. Some of the algorithms we have already encountered and some we will encounter in later posts such as the Logistic Regression, Naive Bayes, Adaboost, Gradient Boosting, KNN, Support Vector Machines, Neural Networks etc. In this […]