Book Image

Machine Learning Algorithms - Second Edition

Book Image

Machine Learning Algorithms - Second Edition

Overview of this book

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Table of Contents (19 chapters)

Support Vector Regression

SVMs can also be efficiently employed for regression tasks. However, it's necessary to consider a slightly different loss function that can take into account the maximum discrepancy between prediction and the target value. The most common choice is the ε-insensitive loss (which we've already seen in passive-aggressive regression):

In this case, we consider the problem as one of a standard SVM where the separating hyperplane and the (soft) margins are built sequentially to minimize the prediction error. In the following diagram, there's a schema representing this process:

Example of Support Vector Regression; the empty circles represent two support vectors

The goal is to find the optimal parameters so that all predictions lie inside the margins (which are controlled by parameter ε). This condition minimized the ε-insensitive...