ABC's repositories
SentimentPolarityAnalysis
情感极性分析repository1：基于情感词典、k-NN、Bayes、最大熵、SVM的情感极性分析。
fast-rcnn
Fast R-CNN
rfecvNano
The development of advection–dispersion particle transport models (PTM) for transport of nanoparticles in porous media has focused on improving model fit by inclusion of empirical parameters. However, this has done little to disentangle the complex behavior of nanoparticles in porous media and to provide mechanistic insights into nanoparticle transport. The most prominent limitation of current PTMs is that they do not consider the influence of physicochemical conditions of the experiments on the transport of nanomaterials. Here, we overcome this limitation by bypassing traditional advection–dispersion PTMs and relating the physicochemical conditions of the experiments to the experimental outcome using ensemble machine-learning methods. We identify a small set of factors that seem to determine the transport of nanoparticles in column experiments by recursive feature elimination with cross validation. .
kaggle
A collection of Kaggle solutions. Not very polished.
parameter_server
moved to https://github.com/dmlc/ps-lite
Hosts
无障碍上网-你~懂得！[更新频率7d，需要请 fork]
redis-py
Redis Python Client
chinese_sentiment
中文情緒分析
spark-mrmr-feature-selection
Machine learning enhancements to Spark MlLib
LogisticRegression
逻辑斯谛回归（Logistic Regression）的python实现，使用牛顿法
courses
Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1
SparkR-pkg
R frontend for Spark
httr
httr: a friendly http package for R
SparkFeatureSelection
Generic implementation of Information Theory-based Feature Selection methods. It also contains an Entropy Minimization Discretization implementation, as well as two artificial dataset generators.
ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
RHadoop
RHadoop - rhadoop@revolutionanalytics.com
ExData_Plotting1
Plotting Assignment 1 for Exploratory Data Analysis
datasharing
The Leek group guide to data sharing
Dynamic-Ensemble-Model
Programming a Feature Selection based Dynamic Transfer Ensemble Model implementing Recursive Feature Elimination Support Vector Machines. Using this model, implementing how Transfer Machine Learning can be efficiently used to process customer data in both source and target domains applied on a Customer Churn Prediction Data-Set Tools/Languages/OS Used: JAVA, LibSVM toolkit, JAVA-ML toolkit, Eclipse IDE, Weka toolkit, Ubuntu 11.10
hadoop-R
Example code for running R on Hadoop