VIEWS: 6 PAGES: 13 POSTED ON: 11/27/2010
INTRODUCTION TO MACHINE LEARNING Slide 2 • Knowledge representation • Definition of machine learning • Sample digitizing • Learning types • Learning paradigma • Learning strategies Knowledge Representation In this part we will mainly focus on digitizing the information All the events in the world are needed to be described as some digital or analog numbers Examples For the currency prediction: The Inputs: I1: The currency value for this term I2: Inflation rate I3: Consumer index I4: Stock index I5: Periodically exportation inrease/decrease rate The Output: O: The currency value for the next term Sexuality: For male “1” otherwise “0”. If the wife or husband works “1” otherwise “0” Job: Teacher: 0, Professor: 1, In the bank: 2, Engineer: 3, Marketting: 4, doctor 5 If he or she has his own house “1” otherwise 0” If he has a car “1” otherwise “0” If he has a problem about his payments “1” otherwise “0” FINAL DECISION: Positive “1” Negative (riscy) “0” Machine learning: The machine learning field evolved from the broad field of artificial intelligence, which aims to mimic intelligent abilities to humans by machines. Learning types Learning from the habits Learning from the views Learning from the commands Learning from the samples Learning by the way of analogy Learning from the explanations Learning from the experiments Learning from the discoveries In general, the machine learning systems are designed for 2 purposes: 1- Hetereo-association: By using all learning types mentioned above, trying to describe the sistem generally based on learning. All samples are evaluated and some generalizations are obtained about the problem especially in classification and prediction problem 2- Auto-association: In this case one event is learned and the data, the system had, is used to characterise the problem. In this case the system can describe the missing points. Human face recognition is an example for this. The system can also succeed if there is problem on the image. Learning Paradigmas: Some paradigms and approaches are improved to convert the studies based on learning to machine learning schemes. Some samples are given below: 1- Symbolic procesing 2- Connectionist systems 3- Statistical pattern recognition 4- Genetic algorithms and evolutionary programming 5- Case based learning The Learning Strategies: 1. Supervised learning: A teacher is needed here. The teacher should give to the system input and output data for the problem to be learned.i.e. Multilayer neural networks. 2. Reinforcement learning: Here the teacher just helps to the system. But, instead of showing all outputs for the inputs set, the teacher expects the system to produce the output for the given inputs, and produces a signal about the output is true or false. Based on this signal, the system goes on learning process. i.e. LVQ networks 3- Unsupervised learning: There is not teacher here helping to the learning of the system. Here only the input values are shown to the system, and the system is wanted to learn the relationships among the inputs by itself. This is mostly used for classification problems. However, after the learning process, the labelling has to be done by the user. ie. ART networks are using this strategy when it is learning. 4- Hybrit strategies: Any learning strategy, which uses two or more of these 3 learning strategies. Here, the partially supervised or partially unsupervised learning systems are described. Radial bases neural networks or probabilistic neural networks can be given as examples. Online and Offline Learning On-line learning: The system can work as a real time system. When the system is working, the learning process can go on. The rule in ART network and Kohonen learning rule are from this learning system. Off-line learning: These systems are trained off-line and then used. For any new data the system is trained off-line. i.e. Delta learning rule in neural networks.
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