ucf data mining lab

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)

(Probabilisticlatent Semantic Latent semantic analysis based on probability, LDA (latent dirichletallocation potential Dirichlet model)Association Mining (Association Mining):Apriori,fp-growth (Frequency pattern tree growth frequent pattern trees growth algorithm), Aprioriall,spade.Recommendation engine (recommended engines):DBR (demographic-based recommendation based on demographic recommendations), CBR (

Semantic Latent semantic analysis based on probability, LDA (latent dirichletallocation potential Dirichlet model)Association Mining (Association Mining):Apriori,fp-growth (Frequency pattern tree growth frequent pattern trees growth algorithm), Aprioriall,spade.recommendation engine (recommended engines):DBR (demographic-based recommendation based on demographic recommendations), CBR (Context-basedrecommen

semantic analyses), pLSA (Probabilisticlatent Semantic Latent semantic analysis based on probability, LDA (latent dirichletallocation potential Dirichlet model)Association Mining (Association Mining):Apriori,fp-growth (Frequency pattern tree growth frequent pattern trees growth algorithm), Aprioriall,spade.recommendation engine (recommended engines):DBR (demographic-based recommendation based on demographi

University of brown, the University of brantis and the University of Massachusetts Institute of Technology. The system is mainly applicable to three types of applications: real-time Monitoring applications, data archiving applications, and applications that include historical and current data processing. The system focuses on real-time processing, such as QoS Management, memory-aware operation scheduling,

University of Montreal). Project homepage:http://deeplearning.net/software/pylearn2/Https://github.com/lisa-lab/pylearn2There are other Python machine learning libraries, such as:PMLL (HTTPS://GITHUB.COM/PAVLOV99/PMLL)Pymining (https://github.com/bartdag/pymining)Ease (https://github.com/edx/ease)Textmining (http://www.christianpeccei.com/textmining/)More machine learning libraries can be found through Https://pypi.python.org/pypi.Category:

: Feature of the previous version: Supports Chinese text input, word segmentation, and other operations, as the source data of classification Feature selector with Chi square test) Parameter Adjustment (parameter tuning) supports the xml configuration file Add feature: Added the K-means algorithm for text clustering. Added a supplement-based Naive Bayes algorithm to greatly improve the classification accuracy. Curren

Orange is a component-based machine learning library that can be used for data mining through visual programming or Python scripts. It is applicable to beginners and experts, it can also be applied to bioinformatics and text mining through extension. Orange is a university in ruerya, Slovenia. Of Ljubljana) is an open-source software developed and produced by the

Corpus: is a collection of all the documents we want to analyzeUse Sogou Lab provided corpus, there is a classlist, inside the content is the document number and classification name1. Import ModuleImport OS Import os.pathfilepaths=[] #建立一个空的列表来存放语料库的文件名称, array variable for in os.walk ( "d:\\python\\python Data Mining \\2.1\\SogouC.mini\\Sample")

About SparkSpark is the common parallel of the open source class Hadoop MapReduce for UC Berkeley AMP Lab, Spark, with the benefits of Hadoop MapReduce But unlike MapReduce, the job intermediate output can be stored in memory, thus eliminating the need to read and write HDFs, so spark is better suited for the algorithm of map reduce, such as data mining and machi

When both event a and Event B occur, mutual information is described as follows: It indicates the amount of information provided because event a is associated with Event B. When dealing with classification issues, we can use mutual information to measure the correlation between a feature and a specific category. If more information is available, the greater the correlation between the feature and the category. The opposite is true. Take the corpus of sogou

-recognition-of-handwritten-digi Note: This code has an obvious bug when it comes to creating a CNN. If you see it, I'm going to look at the structural description of the simplified LeNet-5 and find out what the problem is. Literature: http://blog.csdn.net/celerychen2009/article/details/8973218http://www.so.com/s?ie=utf-8src= 360se7_addrq= convolutional Network http://www.baidu.com/s?ie=utf-8f=8rsv_bp=1rsv_idx=1tn=baiduwd= convolutional%20networksrsv_pq=bcacd8ef0009128frsv_t=77f8gk0zxxpw9ai2hxd