splunk machine learning examples

difference from the numerical size of dimension, such as using User behavior Index to analyze the similarity or difference of user value; Cosine similarity is more to differentiate from the direction, but not sensitive to absolute values, more used to use User content scoring to distinguish the similarity and difference of user interest, and also fixed the problem that the measurement standards may exist between users (because the cosine similarity is not sensitive to absolute values). Copyrigh

build the model.In the exponential distribution family expression of the Bernoulli distribution we have known:, thus obtained.Three assumptions for building a generalized linear model: Assuming that the Bernoulli distribution is met, , in Bernoulli distribution The derivation process is as follows:As with the least squares model, the next work is done by gradient descent or Newton's method.Note the above push to the result, recall, in the logistic regression, we choose th

, 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.

: 0300 cost = 0.134895071 W = 0.3842099 B =-0.16695316EPOCH: 0350 cost = 0.128200993 W = 0.37620357 B =-0.10935676EPOCH: 0400 cost = 0.122280121 W = 0.36867347 B =-0.055185713EPOCH: 0450 cost = 0.117043234 W = 0.36159125 B =-0.004236537EPOCH: 0500 cost = 0.112411365 W = 0.3549302 B = 0.04368245EPOCH: 0550 cost = 0.108314596 W = 0.34866524 B = 0.08875148EPOCH: 0600 cost = 0.104691163 W = 0.34277305 B = 0.13114017EPOCH: 0650 cost = 0.101486407 W = 0.33723122 B = 0.17100765EPOCH: 0700 cost = 0.0986

, 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 Markov chainStep, set each word to a state, and then calculate the probability of transitions between statesThis is the proba

second largest corresponding eigenvector (the solution to the eigenvectors are orthogonal). Which λ is our variance, also corresponds to our previous maximum variance theory, that is, to find a projection can make the most difference between the line.Matlab implementation function [Lowdata,reconmat] = PCA(data,k) [Row, col]=size(data); Meanvalue = mean (data);%vardata = var (data,1,1);Normdata = data-Repmat(Meanvalue,[Row,1]); Covmat = CoV (Normdata (:,1), Normdata (:,2));The covariance matrix

This is the study note of Andrew Ng's public course on machine learning. Examples of reality are spam/non-spam, tumors are benign or malignant, and so on. How to classify. I have accumulated an experience from high school mathematics. Assuming that the linear equation is f (x) = 0, then the point to the left of the line is taken to the left of the linear equation

, which isparameterized by its second argument C. Here Myfun isA MATLAB file function such asfunction [F,g]=Myfun (x,c) F= C*x (1)^2+2*x (1) *x (2) + x (2)^2; %function G= [2*c*x (1) +2*x (2) %Gradient2*x (1) +2*x (2)]; To optimize fora specific value of C, first assign the value to C. Then create a one-argument anonymous function, that captures, the value of C and calls Myfun with the arguments. Finally, pass. Thisanonymous function to Fminunc:c=3; %define parameter first options=

; Static{System.out.println ("This is a parent"); }}classChildextendsparent{ Public Static intX=3; Static{System.out.println ("This was a child"); }}The above two examples are also the same line of code, the result is very different, one child has been initialized, the other is not.Parental delegation ModelThe class loader is used to load the class into the Java virtual machine, starting from the JDK1.2 ver

Julia This programming language is the development efficiency of Python, also has the execution efficiency of C, is the programming language that designs for numerical operation. Julia can call C directly, many open source C and FORTRAN libraries are integrated into the Julia Base library. In addition, it also has notebook. Julia tries to replace R, MATLAB, octave and other numerical computing tools. Its syntax is similar to that of other scientific computing languages. In many cases, there is p

An exploration of AWS Machine Learning (1): comprehend-natural language processing service 1. Comprehend Service Introduction 1.1 features The Amazon comprehend service uses natural language processing (NLP) to analyze text. Its use is very simple. Input: text in any UTF-8 format Output: Comprehend outputs a set of entities (entity), a number of keywords (key phrase), which language (Language), w

Everyone seems to be excited about the new neural network architecture of the Capsule Network (capsnet), I am no exception, can not help to use the capsule network to establish a road side traffic signs identification system, this article is the introduction of this process, of course, also includes some basic concepts of the capsule network elaborated. The project, developed using TensorFlow, is based on the Sabour,nicholas Frosst and Geoffrey E. Hinton's paper, "Capsule dynamic routing", which

some examples of beta functions:It is of the following nature:Pareto DistributionThe Pareto principle must have heard it, is the famous long tail theory, Pareto distribution expression is as follows:Here are some examples: the left image shows the Pareto distribution under different parameter configurations.Some of the properties are as follows:ReferenceprmlmlapCopyright NOTICE: This article for Bo Master

initialization by completing the initialization of the static variable and static code block of the class. Examples are as follows:The classes that need to be loaded Reflect.java as follows: Public classReflect {Private intUserName; Private intpassword; Static{System.out.println ("Reflect static Block"); } PublicReflect () {System.out.println ("Reflect Constructs"); } Public intGetUserName () {returnUserName; } Public voidSetusername (int

-frequent, then all its superset (the collection containing the collection) is also infrequent. The advent of the Apriori principle, after knowing that some itemsets are non-frequent, does not need to calculate the superset of the set, effectively avoids exponential growth of the number of itemsets, and calculates frequent itemsets within a reasonable time.2. RealizeApriori algorithm is a method of discovering frequent itemsets. of the Apriori algorithmTwo input parameters are minimum support le

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 Markov chainStep, set each word to a state, and then calculate the prob

machine Learning: A step-by-step approach to understanding reverse communicationTime 2016-09-13 00:35:59 Yong Yuan ' s blog original http://yongyuan.name/blog/back-propagtion.html theme Data mining In reading the reverse-propagation method, I saw this blog post with the reverse propagation through the example a step by step backpropagation Example, in this blog post, the author gives a simple example of the