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Machine Learning Rumination | Undocumented and incorrectly documented machine learning | statinfer.wordpress.com Reviews
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Undocumented and incorrectly documented machine learning
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October | 2014 | Machine Learning Rumination
https://statinfer.wordpress.com/2014/10
Undocumented and incorrectly documented machine learning. Monthly Archives: October 2014. Undocumented Machine Learning (VI): Logistic Regression. October 29, 2014. In this blog post, I will discuss the logistic regression model and show some thought of mine regarding the relation between logistic regression and naive Bayes. 1. Logistic Regression The binary classifier of logistic regression is to assume the label … Continue reading →. Undocumented Machine Learning (VI): Logistic Regression.
Undocumented Machine Learning (I): Linear Regression | Machine Learning Rumination
https://statinfer.wordpress.com/2013/08/05/undocumented-machine-learning-i-linear-regression
Undocumented and incorrectly documented machine learning. Undocumented Machine Learning (II): Kernel Regression →. Undocumented Machine Learning (I): Linear Regression. August 5, 2013. In general, given data. We are interested in fitting a following linear model. Most of the textbook will tell you that we don’t have to worry about the bias term. Since we can always augment the variables by adding an extra dimension as. Then we are only interested in. Means We can equivalently rewrite the ( 1. To derivate...
August | 2011 | Machine Learning Rumination
https://statinfer.wordpress.com/2011/08
Undocumented and incorrectly documented machine learning. Monthly Archives: August 2011. August 24, 2011. I am interested in statistical inference especially Bayesian inference. I am also a fan of various interesting programming languages, especially functional programming languages. This blog is going to be a mixture of programming and statistics stuff. Hope anyone who reads … Continue reading →. Undocumented Machine Learning (VI): Logistic Regression. Undocumented Machine Learning (V): Naive Bayes.
August | 2013 | Machine Learning Rumination
https://statinfer.wordpress.com/2013/08
Undocumented and incorrectly documented machine learning. Monthly Archives: August 2013. Undocumented Machine Learning (V): Naive Bayes. August 25, 2013. In this blog post, I will show that the decision function of Naive Bayes is the same as logistic regression for observation from exponential family. 1. Naive Bayes with Gaussian The basic assumption of Naive Bayes is . The posterior … Continue reading →. Undocumented Machine Learning (IV): Exponential Family. August 24, 2013. August 17, 2013. Last time ...
Efficient Matlab (I): pairwise distances | Machine Learning Rumination
https://statinfer.wordpress.com/2011/11/14/efficient-matlab-i-pairwise-distances
Undocumented and incorrectly documented machine learning. Efficient F#: tail recursive quicksort via continuation monad. Efficient Matlab (II): Kmeans clustering algorithm →. Efficient Matlab (I): pairwise distances. November 14, 2011. The most important thing for efficient Matlab code is VECTORIZATION. Here I will take distance function as an example to demonstrate how accelerate your code by getting ride of “for-loops”. We can naively implement the function by nested for loops:. As a vector in X and.
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Machine Learning Rumination | Undocumented and incorrectly documented machine learning
Undocumented and incorrectly documented machine learning. Undocumented Machine Learning (VI): Logistic Regression. October 29, 2014. In this blog post, I will discuss the logistic regression model and show some thought of mine regarding the relation between logistic regression and naive Bayes. The binary classifier of logistic regression is to assume the label. Following the Bernoulli distribution. The logistic sigmoid function is. Given a data set. Where the Gaussian prior is mostly used. The model in e...
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