Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …

Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

The success of machine learning in a broad range of applications has led to an ever-
growing demand for machine learning systems that can be used off the shelf by non-experts …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

Abstract Model selection and hyperparameter optimization is crucial in applying machine
learning to a novel dataset. Recently, a subcommunity of machine learning has focused on …

This paper offers a review of the artificial intelligence (AI) algorithms and applications
presently being used for smart machine tools. These AI methods can be classified as …

With the advent of automated machine learning, automated hyperparameter optimization
methods are by now routinely used in data mining. However, this progress is not yet …

Metalearning attracted considerable interest in the machine learning community in the last
years. Yet, some disagreement remains on what does or what does not constitute a …

Purpose: Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder
control (NC) is very important. However, the computer-aided diagnosis (CAD) was not …