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
of older generations of objects and the size of each region.
Handlepromotionfailure
Whether to allow the guarantee to allocate memory failure, that is, the whole old generation of space is not enough, and the entire Cenozoic in the Eden and Survivor objects are the extreme conditions of survival.
Parallelgcthreads
The number of threads that are memory-reclaimed when parallel GC is set.
Gctimeration
Parallel Scavenge collector run time as
0. Training Data set: Iris DataSet (Iris DataSet), get URL Https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.dataAs shown, the first four columns of each row of data in the IRIS data set are the petal length/width, the calyx length/width, and the iris in three categories: Setosa,versicolor,virginicaYou can save the dataset with the following example code and display the last 5 rows1 Import
corresponds to different C, while the longitudinal axes represent different gamma.The above diagram shows the use of cross-validation method we choose the least error of the model parameter, we can only select a few different C and γ, compare which parameter combination of the form is better.Relationship between SVM and support vectors with a cross-validation errorOne of the interesting relationships in SVM is that the error of leaving a cross-validation is less than or equal to the scale of th
This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to understand and master machine learning. This course
Because there is a very detailed online blog, so this section will not write their own, write can not write others so good and thorough.jerrylead Support Vector Machine series:Support Vector Machine (i): http://www.cnblogs.com/jerrylead/archive/2011/03/13/1982639.htmlSupport Vector Machine (ii): http://www.cnblogs.com/jerrylead/archive/2011/03/13/1982684.htmlSupp
Machine learning and Data Mining recommendation book listWith these books, no longer worry about the class no sister paper should do. Take your time, learn, and uncover the mystery of machine learning and data mining. machine
(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
Ai is the future, is science fiction, is part of our daily life. All the arguments are correct, just to see what you are talking about AI in the end.
For example, when Google DeepMind developed the Alphago program to defeat Lee Se-dol, a professional Weiqi player in Korea, the media used terms such as AI, machine learning, and depth learning to describe DeepMind'
1. Training error: The error of the learner in the training set, also known as "experience Error"2. Generalization error: The error of the learner on the new sampleObviously, our goal is to get a better learner on a new sample, which is a small generalization error.3. Overfitting: The learner learns the training sample too well, leading to a decline in generalization performance (learning too much ...). Let me think of some people bookworm, reading de
Turn from 70271574AI (AI) is the future, is science fiction, is part of our daily life. All the assertions are correct, just to see what you are talking about AI in the end.For example, when Google DeepMind developed the Alphago program to defeat the Korean professional Weiqi master Lee Se-dol, the media in the description of the victory of DeepMind used AI, machine learning, deep
predictions. Machine learning helps us predict the world around us.From driverless cars to stock market forecasts to online learning, machine learning has been used in almost every area of self-improvement through prediction. Thanks to the practical use of
,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
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
Li Hang, chief scientist at Huawei Noah's Ark lab, delivered a keynote speech.
Li Hang, chief scientist at Huawei Noah's Ark lab
Li Hang said: so far, we have found that the most effective means of AI research in other fields may be based on data. Using machine learning, we can make our machines more intelligent.
At the same time, Li Hang believes that we need a lot of data to learn exactly how much data we
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
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