udemy machine learning

decision trees (decision tree) 4 　　Cited examplesThe existing training set is as follows, please train a decision tree model to predict the future watermelon's merits and demerits.Back to Catalog What are decision trees (decision tree) 5 　　Cited examplesThe existing training set is as follows, please train a decision tree model to predict the future watermelon's merits and demerits.Back to Catalog What are decision trees (decision tree) 6

(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

Recent research on this one thing-the limit learning machine. In many problems, I often encounter two problems, one is classification, the other is regression. To put it simply, the classification is to label a bunch of numbers, and the regression is to turn a number into a number. Here we need to deal with the general dimension of the data is relatively high, in dealing with these two types of proble

1, Xiao Wei's practice road Http://blog.csdn.net/xiaowei_cqu 2, Morning Chenyusi far (Shi Yuhua Beihang University) Http://blog.csdn.net/chenyusiyuan 3, Rachel Zhang (Zhang Ruiqing) 's blog Http://blog.csdn.net/abcjennifer 4. ZOUXY09 (Shaoyi) http://blog.csdn.net/zouxy09 (deep learning, image segmentation, Kinect development Learning, compression sensing) 5, Love CVPR HTTP://BLOG.CSDN.NET/ICVPR 6, focus on

is close to the global minimum. In fact, you can dynamically adjust the learning rate α= constant 1/(number of iterations + constant 2), so that as the iteration, α gradually reduced, in favor of the final convergence to the global minimum value. However, because "constant 1" and "Constant 2" is not OK, so often set α is fixed.How do you judge the convergence of the model as the iteration progresses? Every 1000 or 5,000 samples, the J value of these

assumptions tend to be 0, but the actual labels are 1, both of which indicate a miscarriage of judgment. Otherwise, we define the error value as 0, at which point the value is assumed to correctly classify the sample Y.Then, we can use the error rate errors to define the test error, that is, 1/mtest times the error rate errors of H (i) (xtest) and Y (i) (sum from I=1 to Mtest).Stanford University public Class machine

,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

into the background do not occupy your currentin Redhat6.5When IP is configured , there is no result after network restart or no restartCd/etc/udev/rules.dDelete 70-persistent.rules 70-persistent-net.rulesRetry againto login Mysql-uroot-pwestos with a passwordGrant Select on test.* to [email protected] ' 172.25.49.4 ' identified by ' Westos ' ; Authorized Rpm-q Service Query rpm-e Service DeleteScheduled Tasks can be seen in/var/spool/cronThis article is from the "11889001" blog, please be su

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

This blog records "Machine Learning Combat" (machinelearninginaction) learning process, including algorithmic introduction and Python implementation. SVM (Support vector machine) SVM is a classification algorithm, through the analysis of training set data to find the best separation plane, and then use the flat face to

Support vector machine algorithm in deep learning does not fire up 2012 years ago, in machine learning algorithm is a dominant position, the idea is in the two classification or multi-classification tasks, the category of the super-plane can be divided into many kinds, then which kind of classification effect is the be

the WTW:The essence is similar.Another understanding: If we consider the constraints in SVM as a filtering algorithm, for a number of points in a plane,It is possible that some margin non-conforming methods will be ignored, so this is actually a reduction of the problem of the VC dimension, which is also an optimization direction of the problem.With the condition of M > 1.126, better generalization performance was obtained compared to PLA.Taking a circle midpoint as an example, some partitionin

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