The increase of publicly available bioactivity data in recent years has fueled and catalyzed
research in chemogenomics, data mining, and modeling approaches. As a direct result, over …

Monitoring large computer networks often involves aggregation of various sorts of data that
are distributed across network components. Finding extreme values, counting discrete …

The research reported in this paper is concerned with assessing the usefulness of reinforcment
learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We …

We propose a gossip-based distributed algorithm for Gaussian mixture learning, Newscast
EM. The algorithm operates on network topologies where each node observes a local …

Stylish differences among poets are usually sought in sound and semantics. In human analysis,
the criteria for recognizing stylistic differences are manifold and intermingled. This study …

This paper presents WODAN2.0, a workflow using Deep Learning for the automated detection
of multiple archaeological object classes in LiDAR data from the Netherlands. WODAN2.0 …

Kriging or Gaussian Process Regression is applied in many fields as a non-linear regression
model as well as a surrogate model in the field of evolutionary computation. However, the …

Although the anomaly detection problem can be considered as an extreme case of class
imbalance problem, very few studies consider improving class imbalance classification with …

In business and academia we are continuously trying to model and analyze complex
processes in order to gain insight and optimize. One of the most popular modeling algorithms is …

Although the class-imbalance classification problem has caught a huge amount of attention,
hyperparameter optimisation has not been studied in detail in this field. Both classification …