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This is already the third algorithm of machine learning. Speaking of the simple Bayes, perhaps everyone is not very clear what. But if you have studied probability theory and mathematical statistics, you may have some idea of Bayesian theorem, but you can't remember where it is. Yes, so important a theorem, in probability theory and mathematical statistics, only a very small space to introduce it. This is n
is all 0. And because it can be deduced that b=1nz∗zt=wt∗ (1NX∗XT) w=wt∗c∗w, this expression actually means that the function of the linear transformation matrix W in the PCA algorithm is to diagonalization the original covariance matrix C. Because diagonalization in linear algebra is obtained by solving eigenvalue and corresponding eigenvector, the process of PCA algorithm can be introduced (the process is mainly excerpted from Zhou Zhihua's "machine
Vi. more hyper-parameters in grid search and K-nearest algorithmVii. Normalization of data Feature ScalingSolution: Map all data to the same scaleViii. the Scaler in Scikit-learnpreprocessing.pyImportNumPy as NPclassStandardscaler:def __init__(self): Self.mean_=None Self.scale_=NonedefFit (self, X):"""get the mean and variance of the data based on the training data set X""" assertX.ndim = = 2,"The dimension of X must be 2"Self.mean_= Np.array ([Np.mean (X[:,i]) forIinchRange (x.shape[1]))
1. The complete course of statistics all of statistics Carnegie Kimelon Wosseman
2. Fourth edition, "Probability Theory and Mathematical Statistics" Morris. Heidegger, Morris H.degroot, and Mark. Schevish (Mark j.shervish)
3. Introduction to Linear algebra, Gilbert. Strong--Online video tutorials are classic
4. "Numerical linear algebra", Tracy Füssen. Lloyd and David. Bao
Textbooks suitable for undergraduates
5. Predictive data analysis of machine
Analytical:Two categories: Each classifier can only divide the samples into two categories. The prison samples were warders, thieves, food-delivery officers, and others. Two classifications certainly won't work. Vapnik 95 proposed to the basis of the support vector machine is a two classification classifier, this classifier learning process is to solve a positive and negative two classification derived fro
http://blog.csdn.net/pipisorry/article/details/44119187Machine learning machines Learning-andrew NG Courses Study notesMachine Learning System DesignPrioritizing what do I do on priorityError analysisError Metrics for skewed Classes Error metrics with biased classesTrading Off Precision and recall weigh accuracy and recall rateData for machines
Nonlinear Transformation (nonlinear conversion)
ReviewIn the 11th lecture, we introduce how to deal with two classification problems through logistic regression, and how to solve multiple classification problems by Ova/ovo decomposition.
Quadratic hypothesesThe two-time hypothetical space linear hypothetical space is extremely flawed:
So far, the machine learning model we have introduced is linear model,
1. From the point of view of physical meaning
What is the recommendation system. For example, for a movie, we predict a user's score on a movie.
We use the symbols shown above to denote. It is worth noting that it is possible that the user did not rate a film, at this time R (i,j) = 0.
Here you need to sort out:For a movie, we need to find some feature space X to measure this movie;For a user, we need to find some parameter H (x) =wtx to predict the user's rating of the film.The problem is that
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, the above classification idea is the idea of SVM. Can be expressed as: SVM is trying to find a super plane to split the sample, the sample in the positive and inverse examples with the super-plane, but not very perfunctory simple separation, but do the best to make the interval between the positive and inverse of the largest margin. In this way, the results of the classification are more credible, and for the unknown new samples have a good classification predictive ability (
floating point number)Returnvalueaddress the address of an operation code in the same methodReference refers to an object in the heap.5.3.2. Length of bytes
The smallest data unit word in a Java virtual machine, which is defined by the implementer of the Java Virtual Machine. However, the size of a word must be sufficient to accommodate byte, short, Int, Char, float, returnvalue, and reference. therefore,
Gradient descent algorithm minimization of cost function J gradient descent
Using the whole machine learning minimization first look at the General J () function problem
We have J (θ0,θ1) we want to get min J (θ0,θ1) gradient drop for more general functions
J (Θ0,θ1,θ2 .....) θn) min J (θ0,θ1,θ2 .....) Θn) How this algorithm works. : Starting from the initial assumption
Starting from 0, 0 (or any other valu
http://blog.csdn.net/zhangyingchengqi/article/details/50969064First, machine learning1. Includes nearly 400 datasets of different sizes and types for classification, regression, clustering, and referral system tasks. The data set list is located at:http://archive.ics.uci.edu/ml/2. Kaggle datasets, Kagle data sets for various competitionsHttps://www.kaggle.com/competitions3.Second, computer vision"Machine
a good effect, basically do not know what method of time can first try random forest.SVM (Support vector machine)
The core idea of SVM is to find the interface between different categories, so that the two types of samples as far as possible on both sides of the surface, and the separation of the interface as much as possible.
The earliest SVM was planar and limited in size. But using the kernel function (kernel functions), we can make the plane proj
Definition of successive descent method:
For a given set of equations, use the formula:where k is the number of iterations (k=0,1,2,... )The method of finding approximate solution by stepwise generation is called iterative method
If it exists (recorded as), it is said that this iterative method converges, obviously is the solution of the equations, otherwise called this iterative method divergence.
Study the convergence of {}. Introducing Error Vectors:Get:Recursion gets:To inve
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I. Working methods of machine learning
① Select data: Divide your data into three groups: training data, validating data, and testing data
② model data: Using training data to build models using related features
③ validation Model: Using your validation data to access your model
④ Test Model: Use your test data to check the performance of the validated model
⑤ Use model: Use fully trained models to mak
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Octave Machine Learning Common commands
A, Basic operations and moving data around
1. Attach the next line of output with SHIFT + RETURN in command line mode
2. The length command returns a higher one-dimensional dimension when apply to the matrix
3. Help + command is a brief aid for displaying commands
4. doc + command is a detailed help document for displaying commands
5. Who command displays all current
This paper is organized from the "machine learning combat" and Http://write.blog.csdn.net/posteditBasic Principles of Mathematics:
Very simply, the Bayes formula:
Base of thought:
For an object to be sorted x, the probability that the thing belongs to each category Y1,y2, which is the most probability, think that the thing belongs to which category.Algorithm process:
1. Suppose something to be sorted x, it
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