is still published as a reading note, not involving too many code and tools, as an understanding of the article to introduce machine learning.The article is divided into two parts, machine learning Overview and Scikit-learn Brief Introduction, the two parts of close relationship, combined writing, so that the overall length, divided into 1, 22.First, it's about
(Digits.data, - Digits.target, intest_size=0.25, -Random_state=33) to + " " - 3 recognition of digital images using support vector machine classification model the " " * #standardize training data and test data $SS =Standardscaler ()Panax NotoginsengX_train =ss.fit_transform (X_train) -X_test =ss.fit_transform (x_test) the + #Support Vector machine classifier for initializing linear hypothesis ALsvc =lin
() function is used to convert the 32x32 binary image to the 1x1024 vector and the loadimages () function to load the image.Four Test results and methodsThe number of support vectors, the error rate of training set and the error rate of test set are tested with the testdigits () function.After 4 iterations are obtained:Five Kernel functionThe kernel function is the core algorithm of SMV, and for a sample that is linearly non-divided, the original input space can be linearly divided into a new k
Machine Learning is to study how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their own performance. It is the core of artificial intelligence and the fundamental way to make computers intelligent. It is applied in various fields of artificial intel
Machine learning system Design (Building machines learning Systems with Python)-Willi Richert Luis Pedro Coelho General statementThe book is 2014, after reading only found that there is a second version of the update, 2016. Recommended to read the latest version, the ability to read English version of the proposal, Chinese translation in some places more awkward
,m)) return jdef clipAlpha(aj,H,L): if aj > H: aj = H if L > aj: aj = L return ajdef smoSimple(dataMatIn, classLabels, C, toler, maxIter): dataMatrix = mat(dataMatIn); labelMat = mat(classLabels).transpose() b = 0; m,n = shape(dataMatrix) alphas = mat(zeros((m,1))) iter = 0 while (iter
The running result is shown in figure 8:
(Figure 8)
If you are interested in the above code, you can read it. If you use it, we recommend using libsvm.
References:
[1]
before, but you need to define T (Y) here:In addition, make:(t (y)) I represents the first element of the vector T (y), such as: (t (1)) 1=1 (T (1)) 2=01{.} is an indicator function, 1{true} = 1, 1{false} = 0(T (y)) i = 1{y = i}Thus, we can introduce the multivariate distribution of the exponential distribution family form:1.2 The goal is to predict the expectation of T (y), because T (y) is a vector, so the resulting output will also be a desired vector, where each element is:Corresponds to th
11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data11.1 what to do firstThe next video will talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when desi
and makes it 0:
9. Calculation of Lagrange's even function
10. Continue to seek a great
11. Organize target function: Add minus sign
12. Linear Scalable support vector machine learning algorithm
The calculation results are as follows
13. Classification decision function
three, linear and can not be divided into SVM
1. If the data linearity is not divided, then increases the relaxation factor, causes
perhaps this loss function is quite in line with the characteristics of SVM ~Multi-Classification problemMethod One:As shown--each time a category is taken out, other categories are synthesized into a large category, which is treated as a two classification problem. Repeat n times to be OKCons: The category of the line will be biased to the training data of the smaller categoryMethod Two: Simultaneous requestExplain the formula:The left is a point of classification at J XJ multiplied by its own
Machine Learning-multiple linear regression and machine Linear Regression
What is multivariate linear regression?
In linear regression analysis, if there are two or more independent variablesMultivariable linear regression). If we want to predict the price of a house, the factors that affect the price may include area, number of bedrooms, number of floors, and ag
The stronger the fault tolerance, the better.B is the plane's biased forward, W is the plane's normal vector, and the X-to-plane mapping:First of all, the point is the smallest distance from the dividing line, and then ask what kind of W and B, so that the point, the value of the distance dividing line is the largest.After shrinking:and taking it as min, take yi* (W^t*q (xi) + b) = 1 =Machine Learning algor
above question, we can apply the kernel function:Quadratic coefficient q n,m = y n y m z n T z m = y n y m K (x N, x m) to get the Matrix Qd.So, we need not to de the caculation in space of Z, but we could use KERNEL FUNCTION to get znt*zm used xn and XM.Kernel Trick:plug in efficient Kernel function to avoid dependence on d?So if we give the This method a name called Kernel SVM:Let us come back to the 2nd polynomial, if we add some factor into expansion equation, we may get some new kernel fun
take an average of this evaluation mode.It is a useful algorithm to use the F-score algorithm to evaluate both precision and recall rates . The PR of the molecule determines that the precision ratio (P) and recall (R) must be large at the same time to ensure that the F score values are larger. If the precision ratio or recall rate is very low, close to 0, the direct result of the PR value is very low, approaching 0, that is, F score is also very low.At this point we compare three algorithms, we
gradient descent algorithm: linear regression Model: Linear hypothesis:Squared difference cost function:By substituting each formula, the θ0 and θ1 are respectively biased:By substituting the partial derivative into the gradient descent algorithm, we can realize the process of finding the local optimal solution.The cost function of linear regression is always a convex function, so the gradient descent algorithm only has a minimum value after execution." Batch " gradient descent: use
How to select Super Parameters in machine learning algorithm: Learning rate, regular term coefficient, minibatch sizeThis article is part of the third chapter of "Neural networks and deep learning", which describes how to select the value of the initial hyper-parameter in the machi
Tags: introduction baidu machine led to the OSI day split data setI. Introduction TO MACHINE learning
Defined
The machine learning definition given by Tom Mitchell: For a class of task T and performance Metric p, if the computer program is self-perfecting wit
Liblinear instead of LIBSVM
2.Liblinear use, Java version
Http://www.cnblogs.com/tec-vegetables/p/4046437.html
3.Liblinear use, official translation.
http://blog.csdn.net/zouxy09/article/details/10947323/
http://blog.csdn.net/zouxy09/article/details/10947411
4. Here is an article, write good. Transferred from: http://blog.chinaunix.net/uid-20761674-id-4840097.html
For the past more than 10 years, support vector machines (SVM machines) have been the most influential algorithms in
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