statistics and machine learning toolbox

This is a creation in Article, where the information may have evolved or changed. Catalogue [−] Iris Data Set KNN k Nearest Neighbor algorithm Training data and Forecasts Evaluation Python Code implementation This series of articles describes how to use the Go language for data analysis and machine learning. Go Machine

When learning machine learning, we basically use MATLAB and python to write algorithms and perform tests; Recently, thanks to the use of opencv to write homework, we have taken a look at the Machine Learning Library (MLL) of opencv ). Let's take a look at the main components

KNN algorithm of ten Algorithms for machine learningThe previous period of time has been engaged in tkinter, machine learning wasted a while. Now want to re-write one, found a lot of problems, but eventually solved. We hope to make progress together with you.Gossip less, get to the point.KNN algorithm, also called nearest neighbor algorithm, is a classification a

A brief introduction of linear regression algorithmlinear regression is a statistical analysis method using regression analysis in mathematical statistics to determine the quantitative relationship between two or more variables, which is widely used. Its expression is y = W ' x+e,e is a normal distribution where the error obeys the mean value of 0. In regression analysis, only one argument and one dependent variable are included, and the relationship

. ClassificationLogistic regression (logistic regression), logistic regression is the corresponding algorithm under the classification task of linear regression.L2 Norm-logistic regression model:$$ Min_{\omega,c}\space\space\space\frac{1}{2}\omega^{t}\omega + c\sum_{i=1}^{n}log (E^{-y_{i} (X_{i}^{T}\omega + c)} + 1) $$L1 Norm-logistic regression model:$$ Min_{\omega,c}\space\space\space\vert\omega\vert_{1} + c\sum_{i=1}^{n}log (E^{-y_{i} (X_{i}^{T}\omega + c)} + 1) $$　　3. Integrated Learning1. R

approximation and generalized beliefPropagation algorithms.pdfLoopy belief propagation for approximate inference an empirical study.pdfLoopy belief propagationdeletion AP (affinity propagation ): L-BFGS:On the limited memory BFGS method for large scale optimizationscalingIIS:Iis.pdf ========================================================== ======================================Theoretical part:Probability graph (Probabilistic networks ):An Introduction to Variational Methods for graphical mode

Bayesian NetworksCherry Blossom PigSummaryThis article is for the July algorithm (julyedu.com) Lunar machine learning 13th time online note. Bayesian Network, also known as the Reliability network, is the extension of Bayes method, and is one of the most effective theoretical models in the field of uncertain knowledge expression and inference. Bayesian networks are suitable for the expression and analysis

used to measure the difference in the direction of two vectors, which is borrowed from the machine learning to measure the difference between sample vectors.(1) The angle cosine formula of vector A (x1,y1) and Vector B (x2,y2) in two-dimensional space:(2) Angle cosine of two n-dimensional sample points a (x11,x12,..., x1n) and B (x21,x22,..., x2n)Similarly, for two n-dimensional sample points a (x11,x12,..

July Algorithm-December machine Learning online Class -14th lesson Note-em Algorithm July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com?EM expection Maxium Desired Maximum1 cited examples1000 people, Statistics height, 1.75,

------------------------------------------------------------------------------------------------------------------ This article is excerpted from the courseware "Machine Learning" by Andrew Ng of Stanford University. Why is the Department of biological engineering at Ocean University the best? They are incomparable to other schools in studying the environment and basis of marine biology.Sat2 mathematical

Original address: http://www.mimno.org/articles/ml-learn/Written by David MimnoOne of my students recently asked me for advice on learning ML. Here's what I wrote. It ' s biased toward my own experience, but should generalize.My Current Favorite Introduction is Kevin Murphy's book (Machine learning). Might also want to look at books by Chris Bishop (Pattern recog

Naive Bayesian Classification (NBC) is the most basic classification method in machine learning, and it is the basis of the comparison of classification performance of many other classification algorithms, and the other algorithms are based on NBC in evaluating performance. At the same time, for all machine learning me

as:If the covariance matrix is a unit matrix (the independent distribution of each sample vector), the formula becomes:That's the Euclidean distance.If the covariance matrix is a diagonal matrix, the formula becomes the normalized Euclidean distance.(2) The advantages and disadvantages of Markov distance: dimension independent, exclude the interference between the correlations between variables.(3) MATLAB calculation (1 2), (1 3), (2 2), (3 1) of the Markov distance between 22X = [1 2; 1 3; 2 2

Category Name Keywords Supervised Classification Decision tree Information Gain Classification regression tree Gini index, Gini 2 Statistics, pruning Naive Bayes Non-parameter estimation, Bayesian Estimation Linear Discriminant Analysis Fishre identification, feature vector Solution K nearest Similarity measurement: Euclidean distance, block distance, edit

included. Limited to nouns, not perfect for adjectives and verbs.Lexical similarity based on corpus statistics:For example, we can infer the meaning of an unknown English word based on many words and contexts. Corpus statistics are also a similar process. The semantic of a word is counted by the corpus of the Internet. Or have the opportunity to wiki Wikipedia semantic analysis and so on.Word sense disambiguationAfter the semantics are calculated, th

, activating the back of the nerve layer, the final output layer of the nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get

direction of two vectors, which is borrowed from the machine learning to measure the difference between sample vectors.(1) The angle cosine formula of vector A (x1,y1) and Vector B (x2,y2) in two-dimensional space:(2) Angle cosine of two n-dimensional sample points a (x11,x12,..., x1n) and B (x21,x22,..., x2n)Similarly, for two n-dimensional sample points a (x11,x12,..., x1n) and B (x21,x22,..., x2n), a co

(decision boundary) is equivalent to the original linear regression3.1 Parametric SolutionAfter the mathematical form of the model is determined, the rest is how to solve the parameters in the model. One of the most common methods in statistics is the maximum likelihood estimation, which is to find a set of parameters, so that the likelihood value (probability) of our data is greater under this set of parameters. In a logistic regression model, the l

index to optimize, need to consider a number of goals, it may be necessary to integrate them into an expression (such as the average number of error indicators), or to define the satisfaction index and optimization indicators.(4) machine learning is a high-speed iterative process: You may want to try a lot of ideas before the final satisfying scenario comes up.(5) Having development sets, test sets, and si

, temperature) =0.970-(2/5) 1.0-(2/5) 1.0-(1/5) 0.0=.570Gain (Ssunny, Wind) =0.970-(2/5) 1.0-(3/5). 918=.019Five A hypothetical space search in decision tree learningThe hypothetical space in the ID3 algorithm contains all decision trees, which is a complete space for finite discrete-valued functions of existing properties.When the decision tree space is changed, ID3 only maintains a single current hypothesis.The basic ID3 algorithm does not backtrack in the search.The ID3 algorithm uses all of