self operating maps
- 1. GOVERNMENT ENGINEERING COLLEGE RAICHUR-584135 Welcome to Technical Seminar presentation On “Self Organizing Maps” Presented by VAISHNAVI 3GU20CS408 Under the guidance of PROF. SUSHMA T SHEDOLE Asst. Professor, Department of Computer Science & Engineering
- 2. CONTENTS Introduction Statistical Pattern Recognition Unsupervised Pattern Classification Unsupervised Neural Networks Advantages Disadvantages Conclusion References
- 3. Introduction These notes provide an introduction to unsupervised neural networks, in particular Kohonen self-organizing maps; together with some fundamental background material on statistical pattern recognition. We can represent this information, and any subsequent information, in a much reduced fashion . This black box will certainly have learned. Data is collected from a large number of drives those that have been involved in accidents and those that have not.
- 4. Statistical Pattern Recognition Elements of Pattern Recognition The goal of pattern is to classify objects into one of a number of categories or classes. The objects of interest are generically called patterns and may be images, speech signals or entries in a database. It is these patterns, which we can loosely define as the natural structure, that gives meaning to events in the external world.
- 5. Pattern space and vector This graph represents our patterns space that is the two dimensional space within which must lay all possible rowing crew members . There are two groupings of our experimental data or measurements, which we can identity as a group of rowers and a group of coxes.
- 6. (a) Two- dimensional pattern space for rowing crews. (b) Three-dimensional pattern space by augmenting with the additional measurement of age.
- 7. Features and decision spaces It can also be unwise to use the raw data in the case of the OCR system, the 35 individual photodiode voltages. It can sometimes be beneficial to transform the measurements that we make as this can greatly simplify the task of classification.
- 8. Unsupervised Pattern Classification Dimensionality Reduction One important function of statistical pattern recognition is dimensionality reduction. The set of measurements originally taken may be too large and not the most appropriate. What we require is means of reducing the number of dimensions but at the same minimizing any error resulting from discarding measurements.
- 9. (a) Classes “best” separated using transform methods (b)Classes “best” separated using clustering methods
- 10. Unsupervised Neural Networks At the start of Section One, we mentioned supervised learning where the desired output response of a network is determined by a set of targets. The general form of the relationship or mapping between the input and output domains are established by the training data. Networks that are trained without such a teacher learn by evaluating the similarity between the input patterns presented to the network. Networks can be used to perform cluster analysis.
- 11. Advantages Interactive and interesting Can be used for: Estimating need Commissioning service Identifying inequalities Combining data: extension of dataset beyond that held by the Registry
- 12. Disadvantages Maps are distorted because the earth is not flat Maps can distort shape, area, direction, and distance This problem is somewhat solved by different map projections
- 13. CONCLUSION Devised by Kohonen in the early 80's, the SOM is now one of the most popular and widely used types of unsupervised artificial neural network. It is built in a one-or two-dimensional lattice of neurons for capturing the important features contained in an input (data) space of interest. In so doing, it provides a structural representation of the input data by the neurons weight vectors as prototypes. The development of self-organizing map as a neural model is motivated by distinct feature of the human brain.
- 14. REFERENCES Books: 1. Neural Networks by Simon Haykin 2. Fundamentals of artificial neural networks by M.H.Hassoun 3. Introduction to artificial neural networks by J.M.Zurada Sites: 1. www.som.com