statistics and machine learning toolbox

Caffe: A fast neural network frameworkAddress: Https://github.com/BVLC/caffeCCV: A modern computer vision library with C language as its coreAddress: HTTPS://GITHUB.COM/LIULIU/CCVMlpack: Extensible C + + machine learning LibraryAddress: https://github.com/anticlockwOpenCV: Open Source computer Vision LibraryAddress: HTTPS://GITHUB.COM/ITSEEZ/OPENCVRecommender: C-Language library for product recommendations/

: 由于没有找到正确函数形式的模型的误差 由于没有找到最佳参数的模型的误差 由于没用使用足够数据的模型的误差 If the training set is limited, it may not support the model complexity required to solve this problem. The Basic Law of statistics tells us that if we can, we should use all the data instead of sampling. Of course, the more data the better, but more data means the difficulty of acquiring and processing complexity. And when the data is more to a certain extent, the difference is less

:spark1.6.2sql interacting with MySQL dataLesson 7:sparksql java Operation MySQL DataLesson 8:spark Statistics User's collection conversion rateClass 9:spark comb user's collection and order conversion rateLesson 10: End-User collection and order conversion ratesClass 11:spark pipeline construction of stochastic forest regression prediction modelLesson 12:spark Random Forest regression forecast results and stored in MySQLThe comparison between the con

.6.2. Statistics on the degree of fire, that is, the amount and content of sharing.6.3. Explore how the fire, that is, to explore the characteristics of communication.6.4. Then build a predictive model of your own content to see if it will fire.6.5. Finally, a summary.7. Before using the logistic regression method to predict the IPO market, the machine learning i

Shanghai Jiao Tong University Zhang Zhihua teacher's public course "Introduction to Machine learning", Course Link: http://ocw.sjtu.edu.cn/G2S/OCW/cn/CourseDetails.htm?Id=397 for three days, take notes. OK, straight to the subject.(i) Basic Conceptsdata Mining and machine learning essence is matter son, ML more close t

whether the existing data is biased. 3, do not data snooping, you are in the brain of the complexity added to the model. What's more, people habitually take the data set and do a exploratory analysis to see what the statistics are. But if the data used in the analysis process contains your test, then there is a possibility of indirect data snooping. In short, no matter what you do, please split the dataset into train and test and do it again, and onl

Machine Learning extracts rules or patterns from data to convert data into information. The main methods are inductive learning and analytical learning. Data is first preprocessed to form features, and then a model is created based on the features. The machine

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).

algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).

, when a system value is not within the normal range may be a computer system in the presence of abnormal state.Exercise: When we model the system, it causes the abnormal state to be judged as the normal state, then we need to reduce the threshold to avoid miscarriage.Gaussiandistribution:Review the Gaussian distribution of some content, more familiar with can skip directly.pattern and probability distribution functions.The mean variance shows the difference of the Gaussian distribution pattern.

algorithm), GA (Genetic algorithm genetic algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (base

Environment SetupRust Generation WriteData Structure assginment Data structure generationMIPS Generation WritingMachine Learning Job WritingOracle/sql/postgresql/pig database Generation/Generation/CoachingWeb development, Web development, Web site jobsAsp. NET Web site developmentFinance insurace Statistics Statistics, regression, iterationProlog writeComputer C

"Machine Learning Algorithm Implementation" series of articles will record personal reading machine learning papers, books in the process of the algorithm encountered, each article describes a specific algorithm, algorithm programming implementation, the application of practical examples of the algorithm. Each algorith

entire section 1.2 above.4 References and recommended readings Wikipedia on the introduction of AdaBoost: Http://zh.wikipedia.org/zh-cn/AdaBoost; The decision tree of Shambo and AdaBoost Ppt:http://pan.baidu.com/s/1hqepkdy; Shambo the PPT:HTTP://PAN.BAIDU.COM/S/1KTKKEPD of AdaBoost index loss function derivation (page 85th ~ 98th); "Statistical learning Method Hangyuan Li" the 8th chapter; Some humble opinions about AdaBoost: http

It should be this time last year, I started to get into the knowledge of machine learning, then the introductory book is "Introduction to data mining." Swallowed read the various well-known classifiers: Decision Tree, naive Bayesian, SVM, neural network, random forest and so on; In addition, more serious review of statistics,

One: The purpose of GBDT algorithm machine learning GBDT algorithm is a supervised learning algorithm. The supervised learning algorithm needs to address the following two questions: 1. The loss function is as small as possible, so that the objective function can conform to the sample 2. The regularization function p

on the training set when the node is pruned, and, where S is the leaf node of the T node.Here, we see the error distribution as a two-item distribution, which is explained by the "normal approximation of the two-item distribution" above, which is biased and therefore requires a continuous correction factor to correct the data.R ' (t) =[e (t) + 1/2]/n (t)And, where S is the leaf node of the T node, the number of all the leaf nodes that you don't know that sign is TFor simplicity, we only use the

Tags: basic machine learning Based on the similarity of functions and forms of algorithms, we can classify algorithms, such as tree-based algorithms and neural network-based algorithms. Of course, the scope of machine learning is very large, and it is difficult for some algorithms to be clearly classified into a certa

classification algorithm, The result type of the target variable is usually a nominal type, and in the regression algorithm it is usually continuous; ③ knowledge representation can take the form of a rule set, or it can be in the form of a probability distribution. Another task of machine learning is regression, which is mainly used to predict numerical data. Classification and regression belong to sup