This page lists all tutorials available for the Shark machine learning
library. The tutorials are ordered by the library modules they belong
to. They cover only a tiny part of the library, but together with
the more than 60 example programs they serve as starting points for
learning how to develop machine learning software using Shark.
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Installation and Compilation under Unix-based Systems
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Installation and Compilation under Microsoft Windows
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Installation of Shark (Windows)
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This tutorial explains the installation of the Shark library
step by step. The tutorial covers the Windows operating system
with Developer Studio environment.
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Shark Examples (Windows)
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Step by step instructions how to translate and execute the
example programs coming with the Shark library.
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Shark GUI Examples (Windows)
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Step by step instructions how to translate and execute the
GUI example programs using QT and Qwt.
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Array
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Using Arrays
| This tutorial introduces the very basic features of the
Array data structure and exception handling.
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Rng
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LinAlg
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Matrix Inversion
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Matrix inversion is an important prerequisite for many algorithms.
This tutorial shows how invert a matrix with Shark.
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Vectors and Matrices
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This tutorial introduces the classes Vector and Matrix
which provide an object oriented interface to the
LinAlg module.
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EALib
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The EALib
| This tutorial explains the basic data structures and
concepts of the EALib.
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Travelling Salesman Problem
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The tutorial introduces the well known traveling salesman
problem and shows how the optimal solution to this problem
can be approximated with a simple genetic algorithm (GA).
This example reproduces a classical experiment and does not
reflect the state-of-the-art in evolutionary algorithms
for TSP.
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The (1+1)-CMA Evolution Strategy
| This short tutorial shows how to do continous optimization using
the (1+1)-CMA Evolution Startegy.
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ReClaM
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Software Architecture
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This tutorial explaines the ReClaM's software architecture
and demonstrates its benefits.
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Linear Discriminant Analysis (LDA)
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The Linear Discriminant Analysis method is used to solve a
multi-class classification problem.
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Feed-forward Neural Network I
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This tutorial shows how to train a standard feed-forward multi-layer
perceptron network to solve the XOR problem.
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Feed-forward Neural Network II
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This mini-tutorial deals with advanced techniques for
neural network training.
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Support Vector Machines
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This tutorial teaches how to train and use a
Support Vector Machine (SVM). The example demonstrates
the binary classification SVM on toy data.
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Approximation of SVMs
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In this tutorial you can learn how to reduce the number of
support vectors used by an SVM model after training.
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FileUtil
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