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Statistical learning theory
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#	Item
321	Empirical Minimum Bayes Risk Prediction: How to extract an extra few% performance from vision models with just three more parameters Vittal Premachandran* National University of Singapore Add to Reading List Source URL: filebox.ece.vt.edu Language: English - Date: 2014-04-22 19:35:34 Estimation theory Bayesian inference Statistical theory Empirical risk minimization Supervised learning Loss function Bayes estimator Regression analysis Image Statistics Econometrics Machine learning
322	On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning Eiji Mizutani and Stuart Dreyfus Abstract— We present a simple, intuitive argument based on “inva Add to Reading List Source URL: www.ieor.berkeley.edu Language: English - Date: 2008-07-16 21:59:53 Statistics Statistical theory Matrices Science Fisher information Rotation matrix Dimensional analysis Measurement Estimation theory
323	Statistical Inference and Learning- Ex-4 Jonathan Rosenblatt and Boaz Nadler due Feb. 15, 2015 Q1 A continuous time Martingale is a stochastic process defined as follows. Let X(t) be a sequence of random variables with a Add to Reading List Source URL: www.wisdom.weizmann.ac.il Language: English - Date: 2015-01-29 08:44:15 Covariance and correlation Data analysis Singular value decomposition Matrices Probability theory Covariance Principal component analysis Martingale Eigenvalues and eigenvectors Statistics Algebra Mathematics
324	Statistical Inference and Learning- Ex-3 Jonathan Rosenblatt and Boaz Nadler due on JanQ1 Let Xi be n i.i.d. samples from a P oisson(λ) distribution. Find an α level most powerful test for testing H0 : λ = 1 Add to Reading List Source URL: www.wisdom.weizmann.ac.il Language: English - Date: 2015-01-15 03:05:51 Exponentials Econometrics Maximum likelihood Sufficient statistic Exponential family Normal distribution Confidence interval Fisher information Conjugate prior Statistics Statistical theory Estimation theory
325	A Formal Theory of Inductive Inference. Part II Ray J. Solomonoff Visiting Professor, Computer Learning Research Center Royal Holloway, University of London IDSIA, Galleria 2, CH–6928 Manno–Lugano, Switzerland Add to Reading List Source URL: world.std.com Language: English - Date: 2014-07-03 18:59:45 Randomness Statistical theory Probability and statistics Markov chain Huffman coding Golomb coding Statistics Information theory Entropy
326	R EGULARIZED L EAST S QUARES AND S UPPORT V ECTOR M ACHINES Francesca Odone and Lorenzo Rosasco RegML 2014 Add to Reading List Source URL: lcsl.mit.edu Language: English - Date: 2014-06-30 18:00:14 Summability methods Mathematical analysis Mathematics Machine learning Support vector machine Regularization Tikhonov regularization Least squares support vector machine Statistics Statistical classification Quantum field theory
327	THE UNIVERSAL DISTRIBUTION AND MACHINE LEARNING Ray J. Solomonoff Visiting Professor, Computer Learning Research Center Royal Holloway, University of London IDSIA, Galleria 2, CH–6928 Manno–Lugano, Switzerland Add to Reading List Source URL: world.std.com Language: English - Date: 2014-09-18 21:25:35 Statistics Statistical inference Probability interpretations Algorithmic information theory Cybernetics Ray Solomonoff Algorithmic probability Inductive inference Kolmogorov complexity Science Artificial intelligence Knowledge
328	M ODEL S ELECTION AND R EGULARIZATION PARAMETER C HOICE R EGULARIZATION M ETHODS FOR H IGH D IMENSIONAL L EARNING Francesca Odone and Lorenzo Rosasco - Add to Reading List Source URL: lcsl.mit.edu Language: English - Date: 2014-07-02 03:10:16 Statistical classification Mathematics Summability methods Learning Regularization Tikhonov regularization Support vector machine Generalization error Solid modeling Statistics Machine learning Quantum field theory
329	6.867 Machine learning, lecture 4 (Jaakkola) 1 The Support Vector Machine and regularization We proposed a simple relaxed optimization problem for ﬁnding the maximum margin sep Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 M-estimators Regression analysis Econometrics Maximum likelihood Loss function Likelihood function Logistic function Logistic regression Regularization Statistics Estimation theory Statistical theory
330	Statistical Inference and Learning- Ex-1 Jonathan Rosenblatt and Boaz Nadler due on Wed. NovQ1 The mean height in Israel is 169cm, with standard deviation of 9cm. What is the probability of an Israeli being tal Add to Reading List Source URL: www.wisdom.weizmann.ac.il Language: English - Date: 2014-11-12 02:00:40 Summary statistics Normal distribution Kullback–Leibler divergence Efficient estimator Standard deviation Statistics Statistical theory Estimation theory

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