machine learning bayes theorem

estimate the intensity of change of a function f near his expectation. It is defined If the variable X itself is considered, the variance of X is also available: Note: (skipped in the book) This equation is actually derived from the definition of variance: In addition, we define two random variables.Covariance: It indicates the degree to which x and y change together. If X and Y are independent of each other, the covariance is 0. We can see that the variance of a single var

If you are not a math department, don't look at this.Because the following is used to demonstrate the correctness of machine learning methods, you can use machine learning to get the results you want. For those who program or use this method, however, you can just use it with confidence and boldness. Just like you know

toolkit. 4. Advanced Data mining And Machine learning SystemADAMS) is a new type of flexible workflow engine designed to quickly establish And maintain a complex knowledge stream in the real world. It is released based on GPLv3. 5. Environment for Developing KDD-Applications Supported by Index-StructureELKI) is a Java-based open-source AGPLv3 data mining software. ELKI is mainly focused on algorithm resear

different from the "standard" case which involves only a single target variable. Meka is based in the WEKA machine learning Toolkit. The advanced Data Mining and machine learning System (ADAMS) are a novel, flexible workflow engine aimed at quickly Buildin G and maintaining real-world, complex knowledge workflows, r

equal to the distance between the other two. This red line is the hyperplane that SVM is looking for in two-dimensional situations. It is used for binary classification data. The point supporting the other two online is the so-called support vector. We can see that there is no sample in the middle of the hyperplane and the other two lines. After finding this hyperplane, we use the mathematical representation of the hyperplane data to perform binary classification of the sample data, which is th

,....} (A is the 1th word in the dictionary and Nip is the No. 35000 Word). So for naive Bayes, it can be expressed as the following matrix (the 1th element of the matrix is 1, and the No. 35000 element is also 1)in the multinomial event model, it is expressed as,. This means that the 1th word of the message is a, and the No. 35000 Word is nip. In this case, if the 3rd word in the message is a, the naive is unchanged, but the representation in the Mul

market analysis. It is used to build systems similar to those offered by Netflix and Amazon, to recommend products to users based on their purchase history, or to build systems that can find all similar articles for a specific period of time. It can also be used to automatically categorize Web pages based on categories (sports, economics, warfare, etc.), or to mark junk e-mail messages. This article does not fully list all the applications of machine

Machine Learning common algorithm subtotalsMachine learning is undoubtedly a hot topic in the field of current data analysis. Many people use machine learning algorithms more or less in their usual work. The IT manager network here summarizes common

generalization error;Easy to explain;Low computational complexity;Disadvantages:It is sensitive to the selection of parameters and kernel functions;The original SVM is only better at dealing with two classification problems;Boosting:Mainly take AdaBoost as an example, first look at the flow chart of AdaBoost, as follows:As you can see, we need to train several weak classifiers during training (3 in the figure), each weak classifier is trained by a sample of different weights (5 training samples

Summaryhave been interested in machine learning, has no time to study, today is just the weekend, have time to see the major technical forum, just see a good machine learning article, here to share to everyone.Machine learning is undoubtedly a hot topic in the field of curre

optimization methods in the case of insufficient information, and why in some cases it will lead to bad consequences, for example, those who have learned machine learning know that the naive Bayes method is not inferior to the Bayesian Network in many cases, but also fast. For example, the higher the dimension of Polynomial Interpolation, the more likely it is t

"Dry" machine learning common algorithm subtotals2015-07-21 Big Data Digest Big Data DigestBig Data DigestNumber Bigdatadigestfunction Introduction Data make the financial, Internet, it changes and subvert the medical, agricultural, catering, real estate, transportation, education, manufacturing and even human itself. To popularize data thinking and disseminate data culture, we have selected the industry's

Turn from:"Basics" Common machine learning Data mining knowledge pointsBasis (Basic):MSE (Mean square error mean squared error), LMS (leastmean square min squared), LSM (Least square Methods least squares), MLE (Maximumlikelihood Estimation maximum likelihood estimation), QP (quadratic programming two-time plan), CP (Conditional probability conditional probability), JP (Joint probability joint probability)

Starter Book List The beauty of mathematics PDFThe author Wu Everyone is familiar with it. The application of mathematics in the fields of machine learning and natural language processing is described in a very popular language. "Programming Collective Intelligence" ("collective Wisdom Programming") PDFAuthor Toby Segaran is also the author of Beautifuldata:the Stories Behind Elegant Data Solutions (t

optimization of the hyper-parameters.　　The following algorithms are used primarily:Classification:· Random Forest· Gbm· Logistic Regression· Naive Bayes· Support Vector Machines· K-nearest NeighborsRegression:· Random Forest· Gbm· Linear Regression· Ridge· Lasso· SvrWhat parameters should I optimize? How can I select the most matching parameters? This is the two problems that people think about the most. It is not possible to answer this question wit

the samples belonging to Class B are in the middle range).The main disadvantage of DT is that it is easy to fit, which is why the integrated learning algorithms such as random Forest, RF, or boosted trees are mentioned. In addition, RF is often the best in many classification problems (I personally believe that generally better than SVM), and the speed can be expanded, and not like SVM need to adjust a large number of parameters, so the recent RF is

Original: http://www.itongji.cn/article/06294DH015.htmlMachine learning methods are very much, but also very mature. I'll pick a few to say.the first is SVM. Because I do more text processing, so more familiar with SVM. SVM is also called Support vector machine, which maps data into multi-dimensional space in the form of dots, and then finds the optimal super-plane which can be classified, and then classifi

Read Catalogue 1. Learning Style 1.1 Supervised learning 1.2 Non-supervised learning 1.3 Semi-supervised learning 1.4 Intensive Learning 2. Algorithm classification 2.1 Regression algorithm 2.2 Instance-based algorithms 2.3 Regulariza

the regression algorithm) , which adjusts the algorithm according to the complexity of the algorithm. The regularization method usually rewards the simple model and punishes the complex algorithm. common algorithms include: Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), and elastic networks (Elastic Net).Decision Tree Learning　　Decision Tree algorithm uses tree structure to establish decision-making model according to the

Tree), GBDT (Gradient boostingdecision tree gradient descent decision Trees), CART (Classificationand Regression tree classification regression trees) , KNN (k-nearest Neighbor K nearest neighbor), SVM (Support Vectormachine), KF (kernelfunction kernel functions polynomialkernel function polynomial kernel functions, Guassian kernelfunction Gaussian kernel function/radial basisfunction RBF radial basis function, string Kernelfunction string kernel function), NB (Naive