Optimization Networks for Integrated Machine Learning
Sprache des Titels:
Englisch
Original Buchtitel:
Lecture Notes in Computer Science
Original Kurzfassung:
Optimization networks are a new methodology for holisti-
cally solving interrelated problems that have been developed with com-
binatorial optimization problems in mind. In this contribution we revisit
the core principles of optimization networks and demonstrate their suit-
ability for solving machine learning problems. We use feature selection in
combination with linear model creation as a benchmark application and
compare the results of optimization networks to ordinary least squares
with optional elastic net regularization. Based on this example we jus-
tify the advantages of optimization networks by adapting the network to
solve other machine learning problems. Finally, optimization analysis is
presented, where optimal input values of a system have to be found to
achieve desired output values. Optimization analysis can be divided into
three subproblems: model creation to describe the system, model selec-
tion to choose the most appropriate one and parameter optimization to
obtain the input values. Therefore, optimization networks are an obvious
choice for handling optimization analysis tasks.