Page 11 - Data Science Algorithms in a Week
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viii               Luis Rabelo, Sayli Bhide and Edgar Gutierrez


                       first and then the basics of ensemble learning are given. Finally, the chapter concludes
                       with a summary of the novel progresses in unsupervised learning.
                          • Deep Learning and a Complex Application in Parallel Distributed Simulation:
                       is  introduced  in  the  chapter  by  Edwin  Cortes  and  Luis  Rabelo  entitled  “Using  Deep
                       Learning  to  Configure  Parallel  Distributed  Discrete-Event  Simulators.”  The  authors
                       implemented  a  pattern  recognition  scheme  to  identify  the  best  time  management  and
                       synchronization  scheme  to  execute  a  particular  parallel  discrete  simulation  (DES)
                       problem. This innovative pattern recognition method measures the software complexity.
                       It characterizes the features of the network and hardware configurations to quantify and
                       capture the structure of the Parallel Distributed DES problem. It is an innovative research
                       in deep belief network models.
                          •  Autonomous  Systems:  The  area  of  autonomous  systems  as  represented  by
                       autonomous  vehicles  and  deep  learning  in  particular  Convolutional  Neural  Networks
                       (CNNs)  are  presented  in  the  chapter  “Machine  Learning  Applied  to  Autonomous
                       Vehicles” by Olmer García and Cesar Díaz. This chapter presents an application of deep
                       learning  for  the  architecture  of  autonomous  vehicles  which  are  a  good  example  of  a
                       multiclass  classification  problem.  The  authors argue that  the  use  of  AI  in  this domain
                       requires two hardware/software systems: one for training in the cloud and the other one in
                       the  autonomous  vehicle.  This  chapter  demonstrates  that  deep  learning  can  create
                       sophisticated models which are able to generalize with relative small datasets.
                          •  Genetic  Algorithms  &  Support  Vector  Machines:  The  utilization  of  Genetic
                       Algorithms (GAs) to select which learning parameters of AI paradigms can actually assist
                       researchers in automating the learning process is discussed in the chapter “Evolutionary
                       Optimization of Support Vector Machines Using Genetic Algorithms”. Fred Gruber uses
                       a  GA  to  find  an optimized  parameter  set  for  support  vector  machines.  GAs and  cross
                       validation increase the generalization performance of support vector machines (SVMs).
                       When doing  this,  it should  be  noted  that  the  processing  time  increases.  However,  this
                       drawback can be reduced by finding configurations for SVMs that are more efficient.
                          •  Texture  Descriptors  for  the  Generic  Pattern  Classification  Problem:  In  the
                       chapter  “Texture  Descriptors  for  the  Generic  Pattern  Classification  Problem”,  Loris
                       Nanni,  Sheryl  Brahnam,  and  Alessandra  Lumini  propose  a  framework  that  employs  a
                       matrix representation for extracting features from patterns that can be effectively applied
                       to very different classification problems. Under texture analysis, the chapter goes through
                       experimental  analysis  showing  the  advantages  of  their  approach.  They  also  report  the
                       results of experiments that examine the performance outcomes from extracting different
                       texture descriptors from matrices that were generated by reshaping the original feature
                       vector. Their new methods outperformed SVMs.
                          •  Simulation  Optimization:  The  purpose  of  simulation  optimization  in  predicting
                       supply chain performance is addressed by Alfonso Sarmiento and Edgar Gutierrez in the
                       chapter  “Simulation  Optimization  Using  a  Hybrid  Scheme  with  Particle  Swarm
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