1. 5.5 Learning algorithms

2. Neural Network inherits their flexibility and computational power from their natural ability to adjust the changing environments. So, they generate internal models of sampled environmental data. Represented in various “structured” weight vectors. NN models have a well defined architecture. Dictated by pattern of connectivity of neurons.

3. Learning Algorithms: define an architecture dependent procedure encode pattern information into weights generate these internal models. Learning encodes pattern information into inter- neuronal connection strengths.

4. Learning Most learning is data driven. The data is the form of a set of input-output patterns. Derived from a possibly unknown classes. Learning problem involves to generate a suitable classification of samples. Learning algorithms are classified into 2 categories. 1. Supervised 2. Unsupervised

5. Supervised Learning Basically it involves function approximation. Learning contains a set of samples(T). T={(X k,D k )} k=1 Input vector: X k £ R n Output vector: D k £R p Explain the behavior of an unknown function f:R n →R p (http://books.google.co.in/books?id=y67YnH4kEMs C&lpg=PA104&pg=PR10#v=onepage&q&f=false)http://books.google.co.in/books?id=y67YnH4kEMs C&lpg=PA104&pg=PR10#v=onepage&q&f=false

6. X k is an unput. Generates the output as S k Use teaching input(D k ) to reduce the error. Design to work with global information. Instructs a behavioristic pattern. NN makes no such assumptions.

7. Unsupervised learning - It involves some form of clustering of data. - Allow self organize method to generate the internal models of NN - Represent the entire data set to a small group of prototypical vectors. - Hold a desired level of discrimination between samples.

8. - New samples are inserted into a system. - So, the prototype will be in a state of constant flux. - No teaching input. - Adaptive vector quantization. The set of data samples {Xi}, X i £ R n has well defined clusters. Clusters define a class of vectors(define in broad sense).

9. - Help establish a classification structure within a set. - no categories are defined in advance. - Quantization vectors are called code book vectors. - The unsupervised learning is self organized. - drived by intra-field neuronal competition and cooperation. - driven by a complex competitive-cooperative process.

10. NN Learning algorithms Operate by iteratively adjusting the weights in the network. The large amount of weights are driven to improve the performance of the network.