Classification is the key and most widely studied paradigm in machine learning community.
The selection of appropriate classification algorithm for a particular problem is a challenging …

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

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 …

We introduce a method to generate vectorial representations of visual classification tasks
which can be used to reason about the nature of those tasks and their relations. Given a …

Given an unsupervised outlier detection task on a new dataset, how can we automatically
select a good outlier detection algorithm and its hyperparameter (s)(collectively called a …

Algorithm selection as well as hyperparameter optimization are tedious task that have to be
dealt with when applying machine learning to real-world problems. Sequential model-based …

Abstract Machine learning and optimisation are two growing fields of artificial intelligence
with an enormous number of computer science applications. The techniques in the former …

The choice of a graph learning (GL) model (ie, a GL algorithm and its hyperparameter
settings) has a significant impact on the performance of downstream tasks. However …

Algorithm selection, a critical process of automated machine learning, aims to identify the
most suitable algorithm for solving a specific problem prior to execution. Mainstream …

To maintain the required service quality of time-critical cloud applications, operators must
continuously monitor their runtime status, detect potential performance anomalies, and …