Predicting user responses, such as click-through rate and conversion rate, are critical in
many web applications including web search, personalised recommendation, and online …

Deep neural networks (DNNs) have begun to have a pervasive impact on various
applications of machine learning. However, the problem of finding an optimal DNN …

Multi-objective optimisation has received considerable attention in recent years as many
real world problems have multiple conflicting objectives. There is an additional layer of …

Abstract In theoretical Machine Learning, the teacher-student paradigm is often employed as
an effective metaphor for real-life tuition. A student network is trained on data generated by a …

We propose a method for designing artificial neural networks (ANNs) for prediction problems
based on an evolutionary constructive and pruning algorithm (ECPA). The proposed ECPA …

Residual generation is an essential part of model-based fault detection schemes. For
nonlinear systems, the task of residual generation is sometimes complicated by the size of …

This study addresses the time series forecasting performance of sparsely connected neural
networks (SCNNs). A novel type of SCNNs is presented based on the Apollonian networks …

In theoretical machine learning, the teacher–student paradigm is often employed as an
effective metaphor for real-life tuition. A student network is trained on data generated by a …

This paper presents a grammatical evolution (GE)‐based methodology to automatically
design third generation artificial neural networks (ANNs), also known as spiking neural …

This paper proposes a new method to model partially connected feedforward neural
networks (PCFNNs) from the identified input type (IT) which refers to whether each input is …