statistics and machine learning toolbox

. Naive Bayesian classifier has two kinds of polynomial model and Bernoulli model when it is used in text classification, and the algorithm realizes these two models and is used for spam detection respectively, which has remarkable performance.Note: Personally, the "machine learning Combat" naive Bayesian chapter on the text classification algorithm is wrong, whether it is its Bernoulli model ("word set") o

scope of this model, such as medical diagnosis and most machine learning. However, it also has some controversy. When it comes to this, it will go back to the topic of debate between the Bayesian School and the frequency School for several hundred years, because the Bayesian school assumes some prior probabilities, in contrast, the frequency school thinks that this anterior is somewhat subjective, and the

do data analysis or ETL work, so the interview must be asked clearly.5. Data Analysis EngineerFrom the title is also seen mainly to do some data statistical analysis of the work, to be honest, before modeling a very important job is to need you to have a full understanding of their data, but the general machine learning post can do data analysis work, or deal with a problem too many steps really troublesom

Based on the literal Relevance Model of Baidu keyword search recommendation tool, this article introduces the specific design and implementation of a machine learning task. Including target setting, training data preparation, feature selection and filtering, and model training and optimization. This model can be extended to Semantic Relevance models, and the design and implementation of Search Engine releva

Supervised learning (supervised learning): The reason to call supervised learning is because we tell the algorithm what we want to predict. The so-called supervision, in fact, is whether our intentions can directly influence the forecast results. Typical representatives: Classification (classification) and regression (regression).Unsupervised

Machine learning is divided into two major categories, supervised learning (supervised learning) and unsupervised learning (unsupervised learning). Supervised learning can be divided in

/bpr.htmlBibliographies on Neural Networkshttp://liinwww.ira.uka.de/bibliography/Neural/Intelligent Motion Control with an Artificial cerebellumHttp://www.q12.org/phd.htmlKernel Machineshttp://www.kernel-machines.org/Some Neural Networks Organizationshttp://www.ieee.org/nnc/http://www.inns.org/Neural Network Modeling in VisionHttp://www.rybak-et-al.net/nisms.htmlNeural Networks and machine learninghttp://learning.cs.toronto.edu/Neural application soft

Last class, we mainly introduced the feasibility of machine learning. First of all, the NFL theorem shows that machine learning is seemingly unworkable. However, after the introduction of statistical knowledge, if the sample data is large enough, and the number of hypothesis is limited, then

Chapter 1 Introduction1.1 What are machine learning?T o Solve a problem on a computer, we need an algorithm. An algorithm was a sequence of instructions that should was carried out to transform the input to output. For example, one can devise a algorithm for sorting. The input is a set of numbers and the output is their ordered list. For the same task, there is various algorithms and we may be interested in

From:http://www.zhizhihu.com/html/y2009/410.html Machine learning is an interdisciplinary area of computer science and statistics, and R on machine learning consists of the following aspects:1) Neural Network (neural Networks): The Nnet packet performs a single hidden layer

Python Machine Learning Practical tutorialsShare Network address--https://pan.baidu.com/s/1miib4og Password: WTIWThe course is really good, share to everyoneMachine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory, stati