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Chapter and Conference Paper
Multi-objective Optimization of a Composite Material Spring Design Using an Evolutionary Algorithm
A multi-objective evolutionary algorithm is applied to optimize the design of a helical spring made out of a composite material. The criteria considered are the minimization of the mass along with the maximiza...
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Chapter and Conference Paper
Learning Manifolds in Forensic Data
Chemical data related to illicit cocaine seizures is analyzed using linear and nonlinear dimensionality reduction methods. The goal is to find relevant features that could guide the data analysis process in ch...
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Chapter and Conference Paper
A Comparison of One-Class Classifiers for Novelty Detection in Forensic Case Data
This paper investigates the application of novelty detection techniques to the problem of drug profiling in forensic science. Numerous one-class classifiers are tried out, from the simple k-means to the more e...
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Chapter and Conference Paper
Large-Scale Clustering through Functional Embedding
We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent...
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Chapter
Deep Learning via Semi-supervised Embedding
We show how nonlinear semi-supervised embedding algorithms popular for use with “shallow” learning techniques such as kernel methods can be easily applied to deep multi-layer architectures, either as a regular...
