machine learning bayes theorem

the analysis of which samples are closer to each other and which samples are far apart, isClusteringProblem.If we want to use the subspace with lower dimensionality to represent the original high-dimensional feature space, then this is the dimensionality reduction problem.Classification RegressionWhether it's classification or regression, it's about building a predictive model. H , given an input x , you can get an output y :

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

the depth of decision tree(2) The structure of the tree changes due to a little change in the sample, which can be improved by integrated learning.Application:(1) Financial options for option pricing are of great use(2) Remote sensing is the application field of pattern recognition based on decision Tree(3) Banks use decision tree algorithm to classify the probability of default payment by loan applicant(4)Gerber Products Inc., a popular baby products company, uses decision tree

Forest　　In order to prevent overfitting, a random forest is equivalent to several decision trees.Four, KNN nearest neighborSince KNN has to traverse all the remaining points each time it looks for the next closest point to it, the algorithm is expensive.V. Naive BayesTo push the probability that the occurrence of event a occurs under B (where events A and B can be decomposed into multiple events), you can calculate the probability of event a occurring under the probability of event B, and then

01 Brief Introduction The probability graph model is the product of the combination of graph theory and probability theory. It was created by Judea Pearl, a famous one. I like the probability graph model tool very much, it is a powerful multi-variable and visualized modeling tool for variable relations, mainly including two major directions: undirected graph model and directed graph model. An undirected graph model is also called a Markov network. It has many applications, such as typical Image

think of such a pattern, but machine learning algorithms can easily (but wrongly) find it. This algorithm utilizes a property of prime numbers (prime numbers) to pay for a variant of the Ma Xiao theorem: In addition to 2, any one prime x satisfies "2 of the x-1 of the second party by its own remainder is 1". For example, 13 is a prime number, and 2 of 13-1 (or 1

Probability statistics The relationship between probability statistics and machine learning Statistic Amount Expect Variance and covariance Important theorems and inequalities Jensen Inequalities Chebyshev on the snow Man's inequality Large number theorem The Central limit

def classifynb (Vec2classify, P0vec, P1vec, PClass1):P1 = SUM (vec2classify * P1vec) + log (PCLASS1)P0 = SUM (vec2classify * P0vec) + log (1.0-PCLASS1)If p1 > P0:Return 1Elsereturn 0Attention:P1vect = log (p1num/p1denom)P0vect = log (p0num/p0denom)>>> p0vArray ([0.04166667, 0.04166667, 0.04166667, 0., 0.,..0.04166667, 0. , 0.04166667, 0. , 0.04166667,0.04166667, 0.125])>>> p1vArray ([0., 0., 0., 0.05263158, 0.05263158,..0., 0.15789474, 0. , 0.05263158, 0. ,0., 0. ])P (W0,W1,W2). WN|CI) = P (w0|c

Java Virtual machine learning-in-depth understanding of the JVM (1)Java Virtual machine learning-slowly pondering the JVM (2)Java Virtual machine learning-slowly pondering the working mechanism of the JVM (2-1) ClassLoaderJava Vir

classifier, supported vector classifierNaive Bayes, Naive BayesK Nearest neighbor classifier, KneighborsclassifierNo parametric model, nonparametric modelsInformation entropy, information gainGini impure, Gini impurityIntegration, EnsembleSingle decision trees, decision treeRandom forest classifier, random forest classifierGradient boost decision tree, gradient tree boostingAverage absolute error, mean absolute error (MAE)Mean square error, mean squa

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

Chapter 1 Introduction1.1 What are machine learning?T o Solve a problem on a computer, we need an algorithm. An algorithm was a sequence of instructions that should was carried out to transform the input to output. For example, one can devise a algorithm for sorting. The input is a set of numbers and the output is their ordered list. For the same task, there is various algorithms and we may be interested in

Scikit-learn this very powerful Python machine learning ToolkitHttp://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.htmlS1. Import dataMost of the data is formatted as M n-dimensional vectors, divided into training sets and test sets. So, knowing how to import vector (matrix) data is the most critical point. We need to use NumPy to help. Suppose the data format is: Stock Prices I

Machine learning methods can be divided into generative and discriminative methods. Generative model: assume that the input is X and the category label is Y. The generative model estimates the joint probability P (X, Y) Because samples can be generated based on the joint probability. Discriminative model: assume that the input is X and the category label is Y. The discriminant model estimates the conditiona

find the joint distribution of the characteristics of the target, but it is convenient to find out the conditional probability distribution between the various features (so as to consider whether it can be sampled only if the conditional probability distribution is known).4. Gibbs sampling　　　　As a result, the flow of the Gibbs sampling algorithm can be obtained in two-dimensional cases as follows　　And in multidimensional cases, such as an n-dimensional probability distribution#x03C0;(x1,x2,...x

Terryj.sejnowski. (c) function interval and geometric interval of support vector machineto understand support vector machines (vectormachine), you must first understand the function interval and the geometry interval. Assume that the dataset is linearly divided. first change the symbol, the category y desirable value from {0,1} to { -1,1}, assuming that the function g is:The objective function H also consists of:Into:wherein, Equation 15 x,θεRn+1, and X0=1. In Equation 16, x,ωεRN,b replaces the

, the message is the probability of classification C, when the word appears more time, will come to the problem of accuracy, you can dissolve the problem into a joint probability, that is, the probability of each word to find P (c| Wi), and then take out the probability of the largest topn to solve, such as n=10,n=15, and so on, the joint probability formula is as follows: p=p1*p2*p3*....pn/(p1*p2*p3*....pn+ (1-P1) * (1-P2) * (1-P3) ... * (1-PN)), where P1-PN is our chosen topn probability.

, 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

say that there are two types of linearity, one is because the variable y is the linear function of the independent variable x, one is because the variable y is the linear function of the parameter Shan. In machine learning, it usually refers to the latter. Third, exponential loss function (Adaboost) People who have studied the AdaBoost algorithm know that it is a special case of the forward step-by algorit

model.Deeper in the brain.Fifth Evolutionary School: a natural learning algorithmDarwin's algorithmExploring: Using the dilemmaProgram of survival of the fittest lawWhat's the use of sex?Congenital and acquiredWhoever learns the fastest will win.Sixth Chapter Bayesian School: In the Church of BayeuxThe theorem governing the worldAll models are wrong, but some of them are useful.From "Eugene Onegin" to Siri