machine learning sample code

existing learning algorithm". The idea of this kind of method is to adapt the multi-marker training sample to the existing learning algorithm, which is to solve the problem of multi-tagging learning.The representative learning algorithm has a first order method binary relevance, which transforms the multi-tagging

hypothetical function when the input space X and the output to Y are known? Solving this problem is divided into two cases, one is in the reversible case, the solution of the problem is very simple, the right portion of Equation 9-10 is set to 0, such as Equation 9-11.(Equation 9-11)which represents the pseudo-inverse of the Matrix X (pseudo-inverse), note that the input matrix X is in rare cases the Phalanx (n=d+1). The form of this pseudo-inverse matrix and the inverse matrix in the square ha

terminology for minimizing we cost function. Algorithm:for t = 1 to M: We get Using code like this is unroll all the elements and put them into one long vector. Using code like this to get back original matrices. Gradient Checking:we can approximate the derivative with respect Toθj as follows: Training: Week 6:applying

) Discriminant analysis is mainly in the statistics over there, so I am not very familiar with the temporary find statistics Department of the Boudoir Honey made up a missed lesson. Here we are now learning to sell. A typical example of discriminant analysis is linear discriminant analysis (Linear discriminant analyses), referred to as LDA. (notice here not to be confused with the implied Dirichlet distribution (latent Dirichlet allocation), although

For a given set of data and problems, the machine learning method to solve the problem is generally divided into 4 steps: A Data preprocessing First, you must ensure that the data is in a format that meets your requirements. The standard data format can be used to fuse algorithms and data sources to facilitate matching operations. In addition, you need to prepare specific data formats for

posteriori, MAP) hypothesis. The method of determining the map assumption is to calculate the posterior probability of each candidate hypothesis with the Bayesian formula. Hmap is the map hypothesis. HMAP≡ARGMAXH∈HP (h| d) =argmaxh∈hp (D|H) P (h) p (d) =argmaxh∈hp (D|H) p (h) Here you can see that the P (D) is removed in the last step because it is not a constant dependent on H. In some cases, we can assume that each assumption in H has the same prior probability, that is, for any hi and HJ, P

1. Decision Tree　　applicable conditions: The data of different class boundary is non-linear, and by continuously dividing the feature space into a matrix to simulate. There is a certain correlation between features. The number of feature values should be similar, because the information gain is biased towards more numerical characteristics.　　Advantages: 1. Intuitive decision-making rules; 2. Nonlinear characteristics can be handled; 3. The interaction between variables is considered.　　Disadvanta

formula shows that if like=c, then the sample may fall within the two interval line, it may also fall on the two interval line above, mainly to see the corresponding relaxation variable value is equal to 0 or greater than 0, the third formula indicates if 0likewTx+b=1 or-1, is the equation, in other places, is an inequality, can not solve B). The representation of the specific visualization is as follows:The KKT condition shows that thelike is not eq

Reprint Please specify source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectory machine Learning Cornerstone Note When you can use machine learning (1) Machine learning Cornerstone Note 2--When you can use

, the above classification idea is the idea of SVM. Can be expressed as: SVM is trying to find a super plane to split the sample, the sample in the positive and inverse examples with the super-plane, but not very perfunctory simple separation, but do the best to make the interval between the positive and inverse of the largest margin. In this way, the results of the classification are more credible, and for

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. deciding what to try next ( Determine what to do next ) I have already introduced some machine learning methods. It is obviously not enough to know the specific process of these methods. The key is to learn how to use them. The so-called best way to master knowledge is to put it into practice. Consider the ear

Twitter-text-Rb-this library can automatically connect and extract user names, lists, and topic tags from Twitter. General Machine Learning Ruby machine learning-some machine learning algorithms implemented by ruby.