'Nearest neighbor classifier' presentation slideshows

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Classifiers

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Quiz 1 on Wednesday

Quiz 1 on Wednesday. ~20 multiple choice or short answer questions In class, full period Only covers material from lecture, with a bias towards topics not covered by projects Study strategy: Review the slides and consult textbook to clarify confusing parts. Project 3 preview.

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2806 Neural Computation Learning Processes Lecture 2

2806 Neural Computation Learning Processes Lecture 2. 2005 Ari Visa. Agenda. Some historical notes Learning Five basic learning rules Learning paradigms The issues of learning tasks Probabilistic and statistical aspects of the learning process C onclusion . Overview .

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Geometrical Complexity of Classification Problems

Geometrical Complexity of Classification Problems. Tin Kam Ho Bell Labs, Lucent Technologies With contributions from Mitra Basu, Ester Bernado, Martin Law. What Is the Story in this Image?. Automatic Pattern Recognition. A fascinating theme.

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Silhouette Analysis-Based Gait Recognition for Human Identification

Silhouette Analysis-Based Gait Recognition for Human Identification. Liang Wang, Tieniu Tan, Huazhong Ning, and Weiming Hu December 2003 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Presenter: Xi Chen. Background and Research Problem.

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Comparison of machine and human recognition of isolated instrument tones

Comparison of machine and human recognition of isolated instrument tones. Ichiro Fujinaga McGill University. Overview. Introduction Exemplar-based learning k-NN classifier Genetic algorithm Machine recognition experiments Comparison with human performance Conclusions.

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Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data

Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Presented by: Tun-Hsiang Yang. purpose of this paper. Compare the performance of different discrimination methods Nearest Neighbor classifier Linear discriminant analysis Classification tree

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Using Word-level Features to Better Predict Student Emotions during Spoken Tutoring Dialogues

Using Word-level Features to Better Predict Student Emotions during Spoken Tutoring Dialogues. Mihai Rotaru Diane J. Litman Graduate Research Competition. Breaking down the (long) title. using. FEATURES. TO PREDICT. BETTER. STUDENT EMOTIONS. TUTORING DIALOGUES. WORD-LEVEL. SPOKEN.

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Review of Assignment 2 (k-nearest neighbor classifier)

Review of Assignment 2 (k-nearest neighbor classifier). CS 175, Fall 2007. Road Map. Assignment 3 (due Thursday) Perceptron classifier Assignment 4 Image manipulation and image classification Assignment 5 Template matching and edge detection in images Assignment 6 Project proposals

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Metagenome analysis

Metagenome analysis. Natalia Ivanova. MGM Workshop February 2, 2012. 1. Metagenome definitions: a refresher course. Metagenome is a collective genome of microbial community, AKA microbiome (native, enriched, sorted, etc.).

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Machine Learning

Machine Learning. Lecture 6 K-Nearest Neighbor Classifier. x 1. 2. 3. 1. 4. 5. 6. 7. 10. 9. 11. 12. 13. 14. 8. x 2. 15. 16. Elliptical blobs (objects). Objects, Feature Vectors, Points. X(15). X(1). X(7). X(16). X(3). X(8). X(25). X(12). X(13). X(6). X(9). X(10).

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Kernels

Kernels. CMPUT 466/551 Nilanjan Ray. Agenda. Kernel functions in SVM: A quick recapitulation Kernels in regression Kernels in k -nearest neighbor classifier Kernel function: a deeper understanding A case study. Kernel Functions: SVM. The dual cost function:. The non-linear classifier

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Application of image processing techniques to tissue texture analysis and image compression

Computer Science Final Year Project 2004. Application of image processing techniques to tissue texture analysis and image compression. Advisor : Dr. Albert Chi-Shing CHUNG. Presented by Group ACH1 (LAW Wai Kong and LAI Tsz Chung). Overview. Introduction Motivation Objectives Results

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Lecture 15. Pattern Classification (I): Statistical Formulation

Lecture 15. Pattern Classification (I): Statistical Formulation. Outline. Statistical Pattern Recognition Maximum Posterior Probability (MAP) Classifier Maximum Likelihood (ML) Classifier K-Nearest Neighbor Classifier MLP classifier.

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Neural network learning of Robot Navigation Tasks

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