Pinned Repositories
ad_examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
America-History
美洲历史
Asia-History
亚洲历史
awesome-domain-adaptation
A collection of AWESOME things about domian adaptation
Biography
传记 自传
cdg
Concept Drift and Anomaly Detection in a Sequence of Graphs
Chinese-History
中国历史
CLRS
:notebook:Solutions to Introduction to Algorithms
Commerce
经济 管理 金融 投资
concept-drift
Algorithms for detecting changes from a data stream.
Cityforest-Material's Repositories
Cityforest-Material/Political-Science
政治
Cityforest-Material/transferlearning
Everything about Transfer Learning and Domain Adaptation--迁移学习
Cityforest-Material/awesome-domain-adaptation
A collection of AWESOME things about domian adaptation
Cityforest-Material/handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
Cityforest-Material/Chinese-History
**历史
Cityforest-Material/Biography
传记 自传
Cityforest-Material/Philosophy
哲学
Cityforest-Material/Psychology
心理学
Cityforest-Material/Social-Sciences
社会科学
Cityforest-Material/Military-Science
军事、军事科学
Cityforest-Material/America-History
美洲历史
Cityforest-Material/Asia-History
亚洲历史
Cityforest-Material/Europe-History
欧洲历史
Cityforest-Material/History-General
历史总论
Cityforest-Material/Logic
逻辑 逻辑学
Cityforest-Material/Commerce
经济 管理 金融 投资
Cityforest-Material/CLRS
:notebook:Solutions to Introduction to Algorithms
Cityforest-Material/MSR
MultiStream Regression
Cityforest-Material/shadowsocksr-android
A ShadowsocksR client for Android
Cityforest-Material/USTC-CS-Courses-Resource
:heart:**科学技术大学计算机学院课程资源(https://mbinary.xyz/ustc-cs/)
Cityforest-Material/ad_examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
Cityforest-Material/pydata-book
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Cityforest-Material/electron-ssr
Shadowsocksr client using electron
Cityforest-Material/stanford-cs229
🤖 Exercise answers to the problem sets from the 2017 machine learning course cs229 by Andrew Ng at Stanford
Cityforest-Material/concept-drift
Algorithms for detecting changes from a data stream.
Cityforest-Material/stanford-CS229-1
This repository provides python solutions to the problem sets of Stanford's graduate course on Machine Learning, taught by Prof. Andrew Ng
Cityforest-Material/cdg
Concept Drift and Anomaly Detection in a Sequence of Graphs
Cityforest-Material/ECHO
ECHO is a semi-supervised framework for classifying evolving data streams based on our previous approach SAND. The most expensive module of SAND is the change detection module, which has cubic time complexity. ECHO uses dynamic programming to reduce the time complexity. Moreover, ECHO has a maximum allowable sliding window size. If there is no concept drift detected within this limit, ECHO updates the classifiers and resets the sliding window. Experiment results show that ECHO achieves significant speed up over SAND while maintaining similar accuracy. Please refer to the paper (mentioned in the reference section) for further details.
Cityforest-Material/SAND
SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream
Cityforest-Material/FUSION
Efficient Multistream Classification using Direct DensIty Ratio Estimation