PPT-Chapter 3 Lazy Learning – Classification Using Nearest Neighbors

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Chapter 3 Lazy Learning Classification Using Nearest Neighbors The approach An adage if it smells like a duck and tastes like a duck then you are probably eating

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Chapter 3 Lazy Learning – Classification Using Nearest Neighbors: Transcript

Chapter 3 Lazy Learning Classification Using Nearest Neighbors The approach An adage if it smells like a duck and tastes like a duck then you are probably eating duck A maxim birds of a feather flock together. Lecture 6. K-Nearest Neighbor Classifier. G53MLE . Machine Learning. Dr . Guoping. Qiu. 1. Objects, Feature Vectors, Points. 2. Elliptical blobs (objects). 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. Lecturer: . Yishay. Mansour. Presentation: Adi Haviv and Guy Lev. 1. Lecture Overview. NN general overview. Various methods of NN. Models of the Nearest Neighbor . Algorithm. NN – Risk Analysis . KNN – . Nearest . Neighbor Method . for Pattern . Recognition. This lecture notes is based on the following paper:. B. . Tang and H. He, "ENN: Extended Nearest Neighbor Method for . Pattern Recognition. ," . by:. Peter Hirschmann. Diagnosing Methods. Monitor symptoms such as:. Resting Tremor. Bradykinesia. Rigidity. Postural Instability. Sub-symptom. Voice Problems. Use classification teaching algorithms to identify Parkinson’s. 1982: -virus, 48,502 bp . 1995: h-influenzae, 1 Mbp . 2000: fly, 100 Mbp. 2001 – present. human (3Gbp), mouse (2.5Gbp), rat. *. , chicken, dog, chimpanzee, several fungal genomes. Gene Myers. Let’s sequence the human genome with the shotgun strategy. Nearest Neighbor Classification. Ashifur Rahman. About the Paper. Authors:. Trevor Hastie, . Stanford University. Robert . Tibshirani. , . University of Toronto. Publication:. KDD-1995. IEEE Transactions on Pattern Analysis and Machine Intelligence (1996). CSC 600: Data Mining. Class 16. Today…. Measures of . Similarity. Distance Measures. Nearest Neighbors. Similarity and Dissimilarity Measures. Used by a number of data mining techniques:. Nearest neighbors. Learn . About You.. Luke K. McDowell. U.S. Naval Academy. http://www.usna.edu/Users/cs/lmcdowel. . Joint work with:. MIDN Josh King, USNA. David Aha, NRL. Bio. 1993-1997: Princeton University. B.S.E., Electrical Engineering. Prabhdeep Singh Virk. Fall 2010. Car buying process. Read reviews, consumer reports from various news agencies.. Consider rankings provided by US News, JD Power etc.. Ask colleagues and friends for recommendation.. . Bayes. Classifier: Recap. L. P( HILSA | L). P( TUNA | L). P( SHARK | L). Maximum . Aposteriori. (MAP) Rule. Distributions assumed to be of particular family (e.g., Gaussian), and . parameters estimated from training data.. Back Ground. Prepared By . Anand. . Bhosale. Supervised Unsupervised. Labeled Data. Unlabeled Data. X1. X2. Class. 10. 100. Square. 2. 4. Root. X1. X2. 10. 100. 2. 4. Distance. Distance. Distances. CS771: Introduction to Machine Learning. Nisheeth. Improving . LwP. when classes are complex-shaped. 2. Using weighted Euclidean or . Mahalanobis. distance can sometimes help. Note: . Mahalanobis. distance also has the effect of rotating the axes which helps. Learning . “K Nearest Neighbor”. Introduction. Classification Algorithms. Decision trees. Rule-based induction. Neural networks. K-Nearest Neighbor (KNN). Random Forests. Bayesian networks. Support Vector Machines. Linear regression, . k-. NN classification. Debapriyo Majumdar. Data Mining – Fall 2014. Indian Statistical Institute Kolkata. August 11, 2014. An Example: Size of Engine . vs. Power. 2. Engine displacement (cc).

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