octave machine learning

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

In recent years, with the rise of big data, cloud computing, mobile Internet, artificial intelligence technology, "machine learning" has become a hot term in the industry. From the field of communication Internet experts, to a variety of enterprises, and even ordinary people, the "machine learning" technology knows. So

Deep learning of wheat-machine learning Algorithm Advanced StepEssay background: In a lot of times, many of the early friends will ask me: I am from other languages transferred to the development of the program, there are some basic information to learn from us, your frame feel too big, I hope to have a gradual tutorial or video to learn just fine. For

Reprinted please indicate Source Address: http://www.cnblogs.com/xbinworld/archive/2013/04/21/3034300.html Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting) The doctor is almost finished. He will graduate next year and start preparing for graduation this year. He feels that he has done a lot of

Original writing. For more information, see http://blog.csdn.net/xbinworld,bincolumns. Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting) The doctor is almost finished. He will graduate next year and start preparing for graduation this year. He feels that he has done a lot of research on

Draw a map, there is the wrong place to welcome correct:In machine learning, features are critical. These include the extraction of features and the selection of features. They are two ways of descending dimension, but they are different:feature extraction (Feature Extraction): creatting A subset of new features by combinations of the exsiting features. In other words, after the feature extraction A feature

Original: Image classification in 5 MethodsAuthor: Shiyu MouTranslation: He Bing Center Image classification, as the name suggests, is an input image, output to the image content classification of the problem. It is the core of computer vision, which is widely used in practice. The traditional method of image classification is feature description and detection, such traditional methods may be effective for some simple image classification, but the traditional classification method is overwhelmed

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

First, Introduction1. Concept : The field of study that gives computers the ability to learn without being explicitly programmed. --an older, informal definition by Arthur Samuel (for tasks that cannot be programmed directly to enable the machine to learn) "A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves wit

same. In addition, it is necessary to feature scale (Features scaling) features before running the gradient descent algorithm.Some options beyond the gradient descent algorithm:In addition to the gradient descent algorithm, there are algorithms that are often used to minimize the cost function, which are more complex and excellent, and typically do not require manual selection of learning rates, and are often faster than gradient descent algorithms.

Recommended book:Data mining: Practical machine learningData mining: Concepts and Techniques Han Jiawei; Read + reference articles later;Machine learning Combat (python);Machine learning Practical Case Analysis (r language);Neural networks and

Environment construction process is very troublesome ... But finally is ready, first give some of the process of reference to the more important information (find Microsoft's machine learning materials is a personal experience, without any reference):1. If the online various numpy, scipy and so on package installation tutorial trouble, go directly to: Microsoft Machine