most common machine learning algorithms

Machine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithmic complexity theory and many other disciplines. Specialized in computer simulation or realization of human learning behavior, in order to a

implied variables obtained by the E step.Repeat 2 steps above until convergence.The formula is as follows:The derivation process of the Nether function in M-Step formula:A common example of the EM algorithm is the GMM model, where each sample is likely to be produced by K-Gaussian, except that each Gaussian produces a different probability, so each sample has a corresponding Gaussian distribution (one of the k's), at which point the implied variable

http://blog.jobbole.com/86131/Common pitfalls in machine learning projects2015/04/22 ·It technology · Machine learningshare to:7 Oracle Technology Carnival Java Implementation Picture watermark Learn to write a word Front-end performance optimization-Basic knowledge cognition This article by

BoostingBoosting in training will give a weight to the sample, and then make the loss function as far as possible to consider those sub-error class samples (such as to the sub-class of the weight of the sample to increase the value)Convex optimizationThe optimal value of a function is often solved in machine learning, but in general, the optimal value of any function is difficult to solve, but the glo

Turn from:"Basics" Common machine learning Data mining knowledge pointsBasis (Basic):MSE (Mean square error mean squared error), LMS (leastmean square min squared), LSM (Least square Methods least squares), MLE (Maximumlikelihood Estimation maximum likelihood estimation), QP (quadratic programming two-time plan), CP (Conditional probability conditional probabili

algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based o

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on

algorithm), GA (Genetic algorithm genetic algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).

The so-called machine learning, in Wikipedia, is a kind of "used to create a dataset for analysis.Program(The specific definition is not mentioned here ). With these methods, we can model events, and often achieve rapid judgment of new data through analysis of existing data. Common machine

gradient descent algorithm (stochastic gradient descent) can be seen as a special case of mini-batch gradient descent, i.e., the parameters in the model are adjusted only one sample at a time in the random gradient descent method, Mini-batch gradient descent, which is equivalent to the B=1 case described above, has only one training sample per Mini-batch.The optimization process of the stochastic gradient descent method is:The random gradient descent is updated once per sample, if the sample si

Common distribution of knowledge points for machine learning and data mining Common Distribution (common distribution): Discrete distribution (discrete type distribution): 0-1 distribution (0-1 distribution) Definition: If a random variable x x only takes 0 0 and 1 12 va

(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

Machine Learning-Overview of common matlab programming commands -- Summary from ng-ml-class octave/MATLAB tutorial CourseraA. basic operations and moving data around1 in command line mode, you can use Shift + press enter to append the next line to output 2 length command to apply to the matrix, and return a higher one-dimensional dimension3 help + command is the

COMMON Pitfalls in machine learningJanuary 6, DN 3 COMMENTS Over the past few years I has worked on numerous different machine learning problems. Along the the I have fallen foul of many sometimes subtle and sometimes is subtle pitfalls when building models. Falling into these pitfalls would often mean when you think

basis of Common machine learning Data mining knowledge points SSE (Sum of squared error, squared error and) SSE=∑I=1N (Xi−x⎯⎯⎯) 2 sse=\sum_{i=1}^{n} (x_i-\overline{x}) ^2 SAE (sum of Absolute error, absolute error and) sae=∑i=1n| xi−x⎯⎯⎯| sae=\sum_{i=1}^{n}| x_i-\overline{x}| SRE (Sum of Relative error, relative error and) Sre=∑i=1nxi−x⎯⎯⎯x⎯⎯⎯sre=\sum_{i=1}^{n}{

Recently there is a mathematical foundation in the system, and next will share some of the most common mathematical functions in machine learning and distributed Python implementations. 1. Logarithmic function Generally, functions Y=logax (a>0, and a≠1) are called logarithmic functions, that is, the power (true number) is the independent variable, the exponent is

intention. Look at the judging criteria below. Using p to express precision,r expression recall; If we choose the criterion = (p+r)/2, then algorithm3 win, obviously unreasonable. Here we introduce an evaluation standard: F1-score. When p = or r=0, there is f=0; When P=1r=1, there is f=1, the largest; Similarly, we apply F1 score to the above three algorithms, and the results are ALGORITHM1 largest, which is the best; algorithm3 the least, the worst

on the learning method of semi-supervised support vector machine Li Yu Zhou Zhihua1 IntroductionIntroduction to 2 semi-supervised support vector machines3 semi-supervised support vector machine learning methodMore than 3.1: large-scale semi-supervised support vector machines for multi-training examples3.2 Fast: Fast s

Objective Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on. Here, the main understanding of supervision and unsu

number D is too large, λ too low, sample size is too small. This provides the basis for us to improve the machine learning algorithm. ============================== Second lecture ============================== Design ====== of ======= machine learning system (i) The design process of the