wsgan001 Goto Github PK

recomm

recommend-system

paper of RS

recommender

rectools__librec

recurrence-rule

Library for working with recurring events

refineandmine

This repository contains the materials concerning the paper : Anytime Interval Patterns Mining with Guarantees

regal

regminer

reinforce4zp

reinforcement-learning

Reinforcement Learning学习之路

reinforcement-learning-algorithms

This repository contains most of classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, A3C, PPO, TRPO. (More algorithms are still in progress)

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

related-papers-for-research

related_items_recommendation

A technique for identifying common patterns, correlations, linkages, and causal structures from data sets stored in various databases, including relational databases, transactional databases, and other forms of data repositories, is known as association rule mining. Association rule mining allows for the finding of interesting connections and linkages among large sets of data items. This rule specifies how frequently a specific item appears in a transaction. A good example is Market Based Analysis. Association rules are critical in data mining for analyzing and forecasting consumer behavior. Customer analytics, market basket analysis, product clustering, catalogue design, and shop layout are all examples of where they're employed. To create machine learning programs, programmers use association rules.

relationclassification-rl

Reinforcement Learning for Relation Classification from Noisy Data(AAAI2018)

reliability-modeling-for-stock-comments

Reliability Modeling for Stock Comment

reltree-eval

reltree-paper

rememberthattime

repeat-consumption

Predicting Consumption Patterns with Repeatedand Novel Events

repeated_pattern_discovery

Implementations for comparative evaluation of algorithms for repeated pattern discovery in multidimensional representation of music. Used for the empirical parts of my master's thesis.

repel

repindepmining

Codes/Experiments for Representation Independence of Structural Similarity Algorithms Over Graph Databases Research

replacement-autoencoder

Keras Implementation of the Paper "Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis".

research

Code for my research...

research-1

Research papers in advanced macroeconomics and financial econometrics

research-2

store the research work

research-handbook-cs

给科研小白的一些资源与工具推荐

research-method

论文写作与资料分享

research-on-vehicle-driving-parameters-modeling-and-mining-of-excellent-driving-behavior

论文《 公共汽车行驶参数建模及优秀驾驶 行为挖掘方法研究》相关程序及数据集，摘要：通过采集公共汽车行驶参数，建立优秀驾驶行为分析模型，可以为驾驶司机提供一种优秀驾车的指导方法；优秀驾驶行为分析模型建立是关键，本文通过建立基于先验规则优秀驾驶行为分析模型和基于聚类分析的优秀驾驶行为挖掘模型，并在建立的两种模型基础上，采用贝叶斯统计方法，对驾驶行为优秀与否建立综合统计评分方法，对驾驶行为进行评判，从而为有效评判司机行为提供依据。 驾驶行为分析方法有许多种，找到适用于公共汽车领域的方法是重点，本文以划分区域路段作为研究出发点，在此基础上建立一种对司机在路段上的评价方法，能够挖掘出同一路段优秀驾驶参数，汇聚参数可以为司机提供针对性的优秀驾车指导。本文主要内容为： ① 介绍了本领域的研究背景和意义，并详细介绍了驾驶行为、基于先验规则、基于关键区域驾驶行为、基于贝叶斯统计的研究现状。 ② 阐述优秀驾驶行为分析模型，并对公交汽车节能驾驶原理和燃料经济性进行了分析，并对本文的关键点优秀驾驶行为模型和模型流程图进行了详细分析和说明。 ③ 建立基于先验规则的下坡空挡滑行、速度档位匹配度、急加速度、急刹车判决模型，并建立基于先验规则的驾驶员综合判决模型；通过对评判概率值进行排序以及阈值来进行优秀驾驶行为评判。 ④ 通过基于关键区域的聚类挖掘，并以油耗作为参考标准，对某一路段驾驶司机参数进行评判，汇聚优秀驾驶行为参数闭包，用来对本路段驾驶司机的优秀行为评判。 ⑤ 以贝叶斯统计原理为基础，综合基于先验规则优秀驾驶行为分析模型获得概率评判值和基于聚类分析的优秀驾驶行为挖掘模型汇聚的优秀驾驶参数闭包评判的驾驶行为概率值，汇总统计获得对驾驶司机的一种综合评判值，通过实验和图示证明了本文所建立模型和应用方法的有效性