ruby machine learning

Analytical:Two categories: Each classifier can only divide the samples into two categories. The prison samples were warders, thieves, food-delivery officers, and others. Two classifications certainly won't work. Vapnik 95 proposed to the basis of the support vector machine is a two classification classifier, this classifier learning process is to solve a positive and negative two classification derived fro

Nonlinear Transformation (nonlinear conversion) ReviewIn the 11th lecture, we introduce how to deal with two classification problems through logistic regression, and how to solve multiple classification problems by Ova/ovo decomposition. Quadratic hypothesesThe two-time hypothetical space linear hypothetical space is extremely flawed: So far, the machine learning model we have introduced is linear model,

Gradient descent algorithm minimization of cost function J gradient descent Using the whole machine learning minimization first look at the General J () function problem We have J (θ0,θ1) we want to get min J (θ0,θ1) gradient drop for more general functions J (Θ0,θ1,θ2 .....) θn) min J (θ0,θ1,θ2 .....) Θn) How this algorithm works. : Starting from the initial assumption Starting from 0, 0 (or any other valu

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

http://blog.csdn.net/zhangyingchengqi/article/details/50969064First, machine learning1. Includes nearly 400 datasets of different sizes and types for classification, regression, clustering, and referral system tasks. The data set list is located at:http://archive.ics.uci.edu/ml/2. Kaggle datasets, Kagle data sets for various competitionsHttps://www.kaggle.com/competitions3.Second, computer vision"Machine

a good effect, basically do not know what method of time can first try random forest.SVM (Support vector machine) The core idea of SVM is to find the interface between different categories, so that the two types of samples as far as possible on both sides of the surface, and the separation of the interface as much as possible. The earliest SVM was planar and limited in size. But using the kernel function (kernel functions), we can make the plane proj

Definition of successive descent method: For a given set of equations, use the formula:where k is the number of iterations (k=0,1,2,... ）The method of finding approximate solution by stepwise generation is called iterative method If it exists (recorded as), it is said that this iterative method converges, obviously is the solution of the equations, otherwise called this iterative method divergence. Study the convergence of {}. Introducing Error Vectors:Get:Recursion gets:To inve

Experimental purposes Recently intend to systematically start learning machine learning, bought a few books, but also find a lot of practicing things, this series is a record of their learning process, from the most basic KNN algorithm began; experiment Introduction Language: Python GitHub Address: LUUUYI/KNNExperiment

I. Working methods of machine learning ① Select data: Divide your data into three groups: training data, validating data, and testing data ② model data: Using training data to build models using related features ③ validation Model: Using your validation data to access your model ④ Test Model: Use your test data to check the performance of the validated model ⑤ Use model: Use fully trained models to mak

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

Which programming language should I choose for machine learning ?, Machine Programming Language Which programming language should developers learn to get jobs like machine learning or data science? This is a very important issue. We have discussed this issue in many forums.

Reprint Please specify the Source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectoryMachine learning Cornerstone Note When machine learning can be used (1)Machine learning Cornerstone Note 2--When you can use machine

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

library wholeheartedly. Scikit Learn: This is a machine learning library written in Python, based on NumPy and scipy. If you are a Python or ruby language programmer, this is for you. The library is user-friendly and powerful, with detailed documentation. If you want to try another library, you can choose orange. Octave: If you are familiar wi

: , where θ is the vector of (n+1) x1, x is the vector of (n+1) x1, ∙. We all use vectors to represent the hyper-plane behind. Except that θ is called a weight, and b is biased, so the complete expression of the super plane is:θ*x+b=0 The Perceptron model can be defined as y=sign (θ∙x+b) where: If we call sign the activation function, the difference between the perceptual machine and the logistic regression is that the sign,logistic regression acti

Brief introduction:Support Vector Machine (SVM) is a supervised learning model of two classification, and his basic model is a linear model that defines the largest interval in the feature space. The difference between him and the Perceptron is that the perceptron simply finds the hyper-plane that can divide the data correctly, and SVM needs to find the most spaced hyper-plane to divide the data. So the per

, R, and Ruby. For Python Machine learning books, I recommend Machine learning in artificial action. Although a little short, it is likely to be a classic in machine learning because it

The topic of machine learning techniques under this column (machine learning) is a personal learning experience and notes on the Machine Learning Techniques (2015) of Coursera public co