PPT-Evaluating which classifiers work best for decoding neural data

Author : willow | Published Date : 2023-11-11

Background Neural decoding neuron 1 neuron 2 neuron 3 neuron n Pattern Classifier Learning association between neural activity an image Background A recent paper

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Evaluating which classifiers work best for decoding neural data: Transcript


Background Neural decoding neuron 1 neuron 2 neuron 3 neuron n Pattern Classifier Learning association between neural activity an image Background A recent paper by Graf et al Nature Neuroscience . Sergey Yekhanin . Microsoft Research. Error-correcting . codes: paradigm. 0110001. 011000100101. 0110001. 01. *. 00. *. 10010. *. Encoder. Decoder. Channel.  . +noise.  .  .  . The paradigm dates back to 1940s (Shannon / Hamming). Usman Roshan. CS 675. Comparison of classifiers. Empirical comparison of supervised classifiers – ICML 2006. Do we need hundreds of classifiers – JMLR 2014. Empirical comparison of supervised classifiers – ICML 2006 . Lifeng. Yan. 1361158. 1. Ensemble of classifiers. Given a set . of . training . examples, . a learning algorithm outputs a . classifier which . is an hypothesis about the true . function f that generate label values y from input training samples x. Given . Ludmila. . Kuncheva. School of Computer Science. Bangor University. mas00a@bangor.ac.uk. . Part 2. 1. Combiner. Features. Classifier 2. Classifier 1. Classifier L. …. Data set. A . . Combination level. . Nathalie Japkowicz. School of Electrical Engineering . & Computer Science. . University of Ottawa. nat@site.uottawa.ca. . Motivation: My story. A student and I designed a new algorithm for data that had been provided to us by the National Institute of Health (NIH).. Machine Learning Algorithms . Mohak . Shah Nathalie . Japkowicz. GE . Software University of Ottawa. ECML 2013, . Prague. “Evaluation is the key to making real progress in data mining”. [Witten & Frank, 2005], p. 143. Linear classifiers on pixels are bad. Solution 1: Better feature vectors. Solution 2: Non-linear classifiers. A pipeline for recognition. Compute image gradients. Compute SIFT descriptors. Assign to k-means centers. BHSAI. Jinbo. Bi, . Ph.D.. HR. SBP. SpO2. MAP. DBP. RR. 0. 2. 4. 6. 8. 10. 12. 14. 16. Time (min). HR. RR. SBP. SpO2. MAP. DBP. 60. 100. 140. 80. 100. 40. 120. 200. 20. 40. 60. 80. mmHg. . % . bpm. Goals for this Unit. Basic. understanding of Neural Networks and how they work. Ability to use Neural Networks to solve real problems. Understand when neural networks may be most appropriate. Understand the strengths and weaknesses of neural network models. Ifeoma. Nwogu. i. on. @. cs.rit.edu. Lecture . 13 . – . Classifiers for images. Schedule. Last class . RANSAC and robust line fitting. Today. Review mid-term. Start classifiers. Readings for today: . Peter D’SENA (and with thanks to David Pace, INDIANA UNIVERSITY for many of the slides in this presentation). september. 2018. From Gatekeeping to Mass Education. Sorting. Educating?. How can we help students ‘invent the university’?. Dr. Sonalika's Eye Clinic in Pune is known for its top-notch eye Specialist surgeons and exceptional eye care services. They offer their services in various locations nearby, including Hadapsar, Amanora, Magarpatta, Mundhwa, Kharadi Rd, Viman Nagar, Wagholi, and Wadgaon Sheri. Experience the best eye care center in Pune. The best clinics for your eye health, include the prestigious Dr. Sonalika Eye Clinic. At Hadapsar, Amanora, Magarpatta, Mundhwa, Kharadi Rd, Viman Nagar, Wagholi, and Wadgaon Sheri Dr. Sonalika's Eye Clinic in Pune is a top choice for individuals in need of exceptional ophthalmologists and eye clinics. They have multiple convenient locations throughout the city, including Hadapsar, Amanora,

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