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)

ν-Support Vector Machines

With real datasets, SVMs can extract a very large number of support vectors to increase accuracy, and this strategy can slow down the whole process. To find a trade-off between precision and the number of support vectors, it's possible to employ a slightly different model called ν-SVM. The problem (with kernel support and n samples denoted by xi) becomes the following:

Parameter ν is bounded between 0 (excluded) and 1, and can be used to control at the same time the number of support vectors (greater values will increase their number) and training error (lower values reduce the fraction of errors). The formal proof of these results requires us to express the problem using a Lagrangian; however, it's possible to understand the dynamics intuitively, considering the boundary cases. When ν → 0, the τ variable...