Shark Tutorials

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

Installation of Shark (SunOS/Linux/MacOS) This tutorial explains the installation of the Shark library step by step. The tutorial covers the installation procedures on UNIX-based systems including Linux and MacOS.
Shark Examples (SunOS/Linux/MacOS) Step by step instructions how to translate and execute the example programs coming with the Shark library.
Shark GUI Examples (SunOS/Linux/MacOS) Step by step instructions how to translate and execute the GUI example programs using QT and Qwt.
Simple Makefiles (SunOS/Linux/MacOS) This tutorial how to build a programs under Linux, SunOS, and MacOS.

Installation and Compilation under Microsoft Windows

Installation of Shark (Windows) This tutorial explains the installation of the Shark library step by step. The tutorial covers the Windows operating system with Developer Studio environment.
Shark Examples (Windows) Step by step instructions how to translate and execute the example programs coming with the Shark library.
Shark GUI Examples (Windows) Step by step instructions how to translate and execute the GUI example programs using QT and Qwt.

Array

Using Arrays This tutorial introduces the very basic features of the Array data structure and exception handling.

Rng

Using Random Number Generators in Shark This tutorial introduces the use of random number generators provided by Shark.

LinAlg

Matrix Inversion Matrix inversion is an important prerequisite for many algorithms. This tutorial shows how invert a matrix with Shark.
Vectors and Matrices This tutorial introduces the classes Vector and Matrix which provide an object oriented interface to the LinAlg module.

EALib

The EALib This tutorial explains the basic data structures and concepts of the EALib.
Travelling Salesman Problem 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.
The (1+1)-CMA Evolution Strategy This short tutorial shows how to do continous optimization using the (1+1)-CMA Evolution Startegy.

ReClaM

Software Architecture This tutorial explaines the ReClaM's software architecture and demonstrates its benefits.
Linear Discriminant Analysis (LDA) The Linear Discriminant Analysis method is used to solve a multi-class classification problem.
Feed-forward Neural Network I This tutorial shows how to train a standard feed-forward multi-layer perceptron network to solve the XOR problem.
Feed-forward Neural Network II This mini-tutorial deals with advanced techniques for neural network training.
Support Vector Machines This tutorial teaches how to train and use a Support Vector Machine (SVM). The example demonstrates the binary classification SVM on toy data.
Approximation of SVMs In this tutorial you can learn how to reduce the number of support vectors used by an SVM model after training.

FileUtil

Handling Configuration Files This tutorial demonstrates how Shark supports the use of configuration files.