1. Pattern Classification and Neural Networks 1.1 The Feature Vector 1.2 Classifiers 1.3 Training a Neural Network 1.3.1 Supervised and Unsupervised Learning 1.4 Performance of Neural Networks 2. Using Libann in your Programs 2.1 Namespaces 2.2 Public Header Files 2.3 Exceptions 2.4 Randomness 2.5 Compiling and Linking with Libann 3. Creating a Feature Vector 4. The Networks 4.1 Persisting the Network 4.2 Kohonen Networks 4.3 Multi-Layer Perceptron Networks 4.3.1 Creating the Network 4.3.2 FeatureMap 4.4 Hopfield Networks 4.5 The Boltzmann Machine 5. Miscellaneous Features 5.1 Version Number 5.2 Logging A. Example Programs A.1 Natural Language Selection Using a Kohonen Network A.2 Character Recognition using a Multi-Layer Perceptron A.3 Style Classification using a Multi-Layer Perceptron A.4 Hopfield Network A.5 The Boltzmann Machine as a Classifier B. Copying B.1 GNU Free Documentation License B.1.1 ADDENDUM: How to use this License for your documents
1.1 The Feature Vector 1.2 Classifiers 1.3 Training a Neural Network 1.3.1 Supervised and Unsupervised Learning 1.4 Performance of Neural Networks
1.3.1 Supervised and Unsupervised Learning
2.1 Namespaces 2.2 Public Header Files 2.3 Exceptions 2.4 Randomness 2.5 Compiling and Linking with Libann
4.1 Persisting the Network 4.2 Kohonen Networks 4.3 Multi-Layer Perceptron Networks 4.3.1 Creating the Network 4.3.2 FeatureMap 4.4 Hopfield Networks 4.5 The Boltzmann Machine
4.3.1 Creating the Network 4.3.2 FeatureMap
5.1 Version Number 5.2 Logging
A.1 Natural Language Selection Using a Kohonen Network A.2 Character Recognition using a Multi-Layer Perceptron A.3 Style Classification using a Multi-Layer Perceptron A.4 Hopfield Network A.5 The Boltzmann Machine as a Classifier
B.1 GNU Free Documentation License B.1.1 ADDENDUM: How to use this License for your documents
B.1.1 ADDENDUM: How to use this License for your documents
