This open access book presents the first comprehensive overview of general methods in
Automated Machine Learning (AutoML), collects descriptions of existing systems based on …

We introduce a framework based on bilevel programming that unifies gradient-based
hyperparameter optimization and meta-learning. We show that an approximate version of …

The matrix-completion problem has attracted a lot of attention, largely as a result of the
celebrated Netflix competition. Two popular approaches for solving the problem are nuclear …

Support vector machine (SVM) has been recently considered as one of the most efficient
classifiers. However, the time complexity of kernel SVM, which is quadratic in the number of …

There have been many attempts to classify imbalanced data, since this classification is
critical in a wide variety of applications related to the detection of anomalies, failures, and …

Time series prediction is an important part of data-driven based prognostics which are
mainly based on the massive sensory data with less requirement of knowing inherent …

Abstract The ChaLearn AutoML Challenge (The authors are in alphabetical order of last
name, except the first author who did most of the writing and the second author who …

This study presents a novel procedure based on ensemble empirical mode decomposition
(EEMD) and optimized support vector machine (SVM) for multi-fault diagnosis of rolling …

Encoding models provide a powerful framework to identify the information represented in
brain recordings. In this framework, a stimulus representation is expressed within a feature …

Calibration was first introduced in 2002 as a new concept to attack the F5 algorithm [3].
Since then, it became an essential part of many feature-based blind and targeted …