• Statistical learning 3: linear support vector machine (SVM)


    learning strategy Soft interval maximization The “linearly separable support vector machine” defined in the previous chapter requires that the training data be linearly separable. However, in practice, the training data often include outliers, so it is often linear and inseparable. This requires us to make some modifications to the algorithm in the previous chapter, that […]

  • Hyperplane of support vector machine for machine learning


    Support vector machine Given training sample set D = {(x_1, y_1), (x_2, y_2),…, (x_m, y_m)}, y_ I \ in {- 1, + 1}, the basic idea of classification learning is to find a partition hyperplane in the sample space based on training set D to separate samples of different categories, but there may be many […]

  • Optimization of support vector machine (linear model) for machine learning


    Move the middle line parallel to both sides until it passes through one or several training sample points.We record the hyperplane as(W, b)。definition:1. Training data and labels (x_1, y_1) (x_2, y_2)… (x_n, y_n), whereX_iIs a vector, y_ I = + 1 or – 12. A training set is linearly separable Training set: {(x_i, y_i)}_ {i […]

  • 1. Logical regression


    1. Logistic regression The essence of logistic regression: assuming that the data obey a certain distribution, the maximum likelihood estimation is used to estimate the parameters. LR is actually a classification. Take the simple dichotomy as an example and assume that the training samples are: $$ \left\{ {\left( {x_1^1,x_2^1} \right),{y^1}} \right\},\left\{ {\left( {x_1^2,x_2^2} \right),{y^2}} \right\},…,\left\{ […]

  • 3. SVM support vector machine


    Idea: find a curve so that the minimum distance between all sample points and this curve is the maximum Distance from point x to line: $$ l = \frac{1}{{\left\| w \right\|}}({w^T}x + b) $$ For two categories, Y values are only – 1 and 1, then the same sign indicates that the classification is correct, […]

  • Machine learning: principle derivation of SVM


    It is said that SVM is the watershed of machine learning, machine learning is just around the corner. This paper will introduce the principle derivation process of SVM in detail, including linear, near linear, nonlinear, optimization methods, etc. a lot of ideas are derived from statistical learning method and zero basis introduction Python data mining […]

  • White board derivation of SVM | derivation process of loss function evolved from maximum interval target


    The English name of SVM is support vector machine, which is called support vector machine. The perceptron learning algorithm will get different hyperplanes because of different initial values. However, SVM tries to find an optimal hyperplane to divide the data. How to calculate the best one? We can naturally think that if the distance from […]

  • KKT and KKT support vector machine learning


    Overview of SVM Support vector machine (SVM) is a supervised classification algorithm, and it mostly deals with the problem of binary classification. First of all, through a series of pictures to understand several concepts about SVM. In the above figure, there are orange dots and blue dots representing the two types of labels. If you […]

  • SVM support vector machine notes 1


    Support vector machine Many lines in this diagram can separate the two points. But which one?Intuitively, we will definitely choose the middle one, because it splits the two parts of data most “open” and has the highest fault tolerance rate. The most marginal point here actually plays a very important role. As long as the […]

  • From linear regression to neural network


    background: I always want to sort out my understanding and thinking of generalized linear model, so I have this essay. premise: 1. First of all, the introduction model will follow theThree elementsTo expand, i.eModel(parameter space of the model),strategy(how to choose the optimal model, generally referred to as cost function / loss function),algorithm(method of model learning […]

  • Machine learning support vector machine (SVM)


    Catalog Support vector machine (SVM) 1. Fundamentals 2. Soft septum 3. Kernel function 4. Sklearn implements SVM 5. SVM multi classification 4.1 multi classification principle 4.2 SVM multi classification based on sklearn PrefaceRefer to machine learning. I don’t understand the dual problem…. I’m just a code Porter Machine learning column: Machine learning – linear regression […]

  • Review of machine learning (11): support vector machine (SVM)


      1. introduction SVM, support vector machine, which is our Chinese name support vector machine, I believe that as long as the children’s shoes have met with machine learning, they have heard the name more or less. As an old member of machine learning family, its classics need not be said. In terms of principle […]