INTERNET PORTALS WITH MACHINE LEARNING

1. INTERNET PORTALS WITH MACHINE LEARNING PRESENTED BY:- AMRIT CHOUDHARY BTECH- CSE 7TH SEM

2. WHY PORTALS ??? • Gather content from Web organize it for easy access, retrieval and search. • Eg. www.twenty19.com, • Disadvantage:- • These portals are difficult and time-consuming to maintain. • Soln… • My project proposes use machine learning techniques to greatly automate creation and maintenance of portals.

3. MACHINE LEARNING Study of computer algorithms that improve automatically through experience.IMPORTANCE OF MACHINE LEARNING:- • 4 general categories task’s which are impossible or difficult. • Problems ,no human expert available. • Human experts available ,no explanation of expertise. • Problems where phenomena changes rapidly. • Applications to be customized for each computer. • HOW MACHINE LEARNS ??? • ASSIGNING WEIGTHS:- • some weight assigned and compared with previous results stored. • DECESION TREES:- • System starts from parent node with techniques of BDS and DPS.

4. FORMAL GRAMMARS:- • CRESTON :- A new rule is constructed by the system or acquired from an external entity • GENERALISATION:- Conditions dropped / made less restrictive, so that the rule applies in a larger number of situations. • SPECIALIZATION:- Additional conditions added to existing conditions made more restrictive, so that the rule applies to specific situations. • APPLICATIONS:- • Optical Character Recognition(OCR) • Face Detection • Spam Filtering • medical diagnosis • spoken language understanding • fraud detection • PLAN:- E-BOOK PORTAL

5. MACHINE LEARNING FEATURES INCLUDED IN PORTALS • CLASSIFICATION INTO TOPIC HIERARCHY:- • Efficiently organize, view and explore large • quantities of information. • SPIDERING:- • Spider efficiently explores Web, following links that are more likely to lead to e-books. • Each reference broken down into appropriate fields, such as author, title, journal, and date. • WEBWATCHER • Tour guide, highlights hyperlinks that it believes will be of interest

6. REINFORCEMENT LEARNING • Learning optimal decision making from rewards or punishment. • Goal of reinforcement learning:- learn a policy, a mapping from states to actions, that maximizes the sum of reward over time. • supervised learning:-Told correct action for particular state • ADVANTAGE OVER SUPERVISED LEARNING:- • instead it is told how good or bad the selected action was, expressed in the form of “scalar reward”.

7. INFORMATION EXTRACTION • Information extraction, identifying phrases of interest in textual data. • powerful way ,summarize’s the information relevant to a user's needs. • Eg. On- topic documents may be several hyperlinks away from the current choice point; but the text on the current page may offer indications of which hyper link will lead to reward soonest. • ADVANTAGE:- • Allow’ssearches over specific fields. • Effective presentation of search result(Shows in bold)

8. CONCLUSION:- • In addition to future work discussed earlier, many • other areas where machine learning can further automate the construction and maintenance of domain-specific search engines. Eg. Text classification can decide which documents on the Web are relevant to the domain. • This paper has shown that machine learning techniques can significantly aid the creation and maintenance of portals and domain-specific search engines.

9. ADVANTAGE’S • These techniques allow portals quick creation with minimal effort. • Performance is based on the rewards over time. • The environment presents situations with delayed rewards. • DISADVANTAGE’S • Backtracking:- algorithm fails to backtrack to the original path resulting in deadlock state. • Specify initial and goal states ,specify rules and modify the rules sometimes if necessary. • If knowledge base of expert system ,not correct or lack facts & figures, solutions thus acquired ineffective.

10. ANY QUERIES ??? …