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A curated list of resources for Online Semi-supervised Learning
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2021_AliCloud_Supply_Chain_Competition
2021阿里云供应链大赛之需求预测及单级库存优化，B榜73名Python
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stock_predict_RNN_LSTM_BP
采用RNN、LSTM、BP模型对股票数据进行预测HTML
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Python_Scrapy
采用Python技术，实现对各大平台的爬取Jupyter Notebook
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LogisticCircuit
这是LG仓库Python
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JavaGit
这是JavaWeb的相关作业Java
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OLDSD
This paper explores a new online learning problem where the data streams are generated from an over-time varying feature space, in which the random variables are of mixed data types including Boolean, ordinal, and continuous. The crux of this setting lies in how to establish the relationship among features, such that the learner can enjoy 1) reconstructed information of the missed-out old features and 2) a jump-start of learning new features with educated weight initialization. Unfortunately, existing methods mainly assume a linear mapping relationship among features or that the multivariate joint distribution could be modeled as Gaussians, limiting their applicability to the mixed data types. To fill the gap, we in this paper propose to model the complex joint distribution underlying mixed data with Gaussian copula, where the observed features with arbitrary marginals are mapped onto a latent normal space. The feature correlation is approximated in the latent space through an online EM process. Two base learners trained on the observed and latent features are ensembled to expedite convergence, thereby minimizing prediction risk in an online learning regime. Theoretical and empirical studies substantiate the effectiveness of our proposed approach. Code is available online at https://github.com/Zhuosd/OLDSD.gitPython
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ARDST
Deep neural networks (DNNs) have driven much of the progress in numer-ous intelligent systems such as natural language processing and autonomous driving. However, trivial and human-unnoticeable adversarial attacks can fool a well-trained DNN to make arbitrarily wrong decisions. The crux of this issue lies in DNNs’ hierarchical layer-by-layer learning structure, where the tiny distortions tend to be escalated infinitely. Although many related de-fense methods are proposed, they all are the 'vaccine' type, i.e., they require prior knowledge of adversarial attacks to design a specific defense strategy. While in real attack scenarios, it is impossible to learn such prior knowledge in advance. To address this issue, this paper proposes a novel 'immune' type learning model from a neuro-symbolic perspective, termed Adversarial-Resilient Deep Symbolic Tree (ARDST). ARDST possesses two unique prop-erties: 1) it is a semi-parametric tree model, where the nodes are logic opera-tors and the weights of edges are the learned parameters, and 2) it can pro-vide a clear reasoning path of how a decision is made in very fine granulari-ty. As such, ARDST can not only defend the various adversarial attack types but also has a much smaller size of parameter space than DNNs. Extensive experiments on three benchmark datasets are carried out. The results sub-stantiate that our ARDST can achieve comparable representation learning ability to that of DNNs on perceptive tasks and, more importantly, is resili-ent to state-of-the-art adversarial attacks, including FGSM, DeepFool, PGD, and BIM.Python
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Car_Plate_Recognition
采用CNN模型对车牌进行识别，包括对应省份简称、字母、数字等三个部分的识别HTML
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Processing_excel
这是处理Excel的全部项目集合，其中包括Excel简单处理，sheet表之间的合并，excel进行合并操作，进行简单的excel运算Jupyter Notebook
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GzUniversity-yiqing
广大疫情自动打卡程序；本程序仅供学习使用，不用于任何商业用途。请勿将本程序进行非法使用。大家一定要听从学校组织安排，不要心存侥幸，瞒报自己真实情况妨碍疫情防护，危害他人生命健康。特此声明：如有人使用本程序用于非法用途，均与本人无关。Python
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Kaggle_houseprice_predict
Kaggle房价数据进行模型预测
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APE-GAN-Code
This is the code of ape-ganJupyter Notebook
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APE-GAN
This is the code of ape-gan
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PCA_Analysis
HTML
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2021_Xiamen_International_Bank_Digital_Creative_Finance_Cup
银行陆续打造了线上线下、丰富多样的客户触点，来满足客户日常业务办理、渠道交易等需求。在实际理财产品业务开展过程中，需要挖掘不同理财产品对客群的吸引力，从而找到目标客群，进行针对性营销。Jupyter Notebook
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Hyperspectrum_images_classification
The integration of spectral and spatial information is crucial in remotely sensed hyperspectral image classification. Some available approaches extract spatial features before classification, while other techniques include spatial information as a spatial regularizer. Due to the model complexity,Python
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Reading_paper
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Scrap_Chain_wealth
Jupyter Notebook
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zhuosd.github.io
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my_ml_project
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Density_peak_clustering-Semi-Supervision
Having a multitude of unlabeled data and few labeled ones is a common problem in many practical ap- plications. A successful methodology to tackle this problem is self-training semi-supervised classification. In this paper, we introduce a method to discover the structure of data space based on find of density peaks. Then, a framework for self-training semi-supervised classification, in which the structure of data space is integrated into the self-training iterative process to help train a better classifier, is proposed. A series of experiments on both artificial and real datasets are run to evaluate the performance of our proposed framework. Experimental results clearly demonstrate that our proposed framework has better performance than some previous works in general on both artificial and real datasets, especially when the distribution of data is non-spherical. Besides, we also find that the support vector machine is particularly suitable for our proposed framework to play the role of base classifier.Python
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zhuosd.github-io
个人简历HTML
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OSLMDF
Online Semi-Supervised Learning with Mix-Typed Streaming FeaturesPython
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