The impact of deep learning on document classification using semantically rich representations

This paper presents a semantically rich document representation model for automatically classifying financial documents into predefined categories utilizing deep learning. The model architecture consists of two main modules including document representation and document classification. In the first module, a document is enriched with semantics using background knowledge provided by an ontology and through the acquisition of its relevant terminology. Acquisition of terminology integrated to the ontology extends the capabilities of semantically rich document representations with an in depth-coverage of concepts, thereby capturing the whole conceptualization involved in documents. Semantically rich representations obtained from the first module will serve as input to the document classification module which aims at finding the most appropriate category for that document through deep learning. Three different deep learning networks each belonging to a different category of machine learning techniques for ontological document classification using a real-life ontology are used.Multiple simulations are carried out with various deep neural networks configurations, and our findings reveal that a three hidden layer feedforward network with 1024 neurons obtain the highest document classification performance on the INFUSE dataset. The performance in terms of F1 score is further increased by almost five percentage points to 78.10% for the same network configuration when the relevant terminology integrated to the ontology is applied to enrich document representation. Furthermore, we conducted a comparative performance evaluation using various state-of-the-art document representation approaches and classification techniques including shallow and conventional machine learning classifiers.     

基于集成学习的科研计划项目绩效预测研究

提出基于集成学习的项目绩效预测方法,利用多分类集成监督学习算法,对网络爬虫得到的已结题项目数据中隐含的关于项目绩效的信息进行有效挖掘,形成项目绩效预测模型.基于国家自然科学基金项目数据,利用多种指标对模型的性能进行评估,将模型对项目的绩效预测结果与专家的评估结果进行比较,结果显示模型的有效性.     

Prediction model of permeability index for blast furnace based on the improved multi-layer extreme learning machine and wavelet transform

The permeability index of the blast furnace is a significant symbol to measure the smooth operation of the blast furnace. This paper proposes a novel prediction model for permeability index of the blast furnace based on the multi-layer extreme learning machine (ML-ELM), the principal component analysis (PCA) method and wavelet transform (called as W-PCA-ML-ELM prediction model). This modified ML-ELM algorithm is based on the ML-ELM algorithm and the PCA method (named as PCA-ML-ELM). The PCA method is applied on the ML-ELM algorithm to improve the algebraic property of the last hidden layer output matrix which deteriorates its generalization performance due to the high multicollinearity. Because the production data of the blast furnace field contain noises, this paper applies the wavelet transform to remove the noise. Comparing with other prediction models which are based on the ML-ELM, the ELM, the BP and the SVM, simulation results illustrate that the better generalization performance and stability of the proposed W-PCA-ML-ELM prediction model.     

社会文化对高新技术企业团队学习行为的影响研究

社会文化影响着人们的价值取向和行为方式。团队学习作为一种组织行为必然受到组织所在的社会文化环境的影响。为了探讨两者之间的关系,对大连软件园区的12家高新技术企业的460名员工进行了问卷调查。研究结果表明:在社会文化的不同类型中,低权力距离、低不确定性规避、集体主义、柔性倾向和长期导向会对不同的团队学习行为产生积极的影响。     

区域创新环境对科技型小微企业创新的影响——基于双元学习的中介作用

科技型小微企业的学习和创新与成熟的大中型科技企业有着根本不同。以区域创新系统作为区域创新环境的基本分析框架,本研究探讨了区域要素环境、文化环境和政策环境对于科技型小微企业的双元学习和创新产品竞争力的影响。基于对珠三角地区253家科技型小微企业的实证分析,研究结果表明,区域创新环境的三个维度对于探索性学习有着显著的正向影响,而区域要素环境和政策环境对于应用性学习有着显著的正向影响;科技型小微企业的探索性学习对于创新产品差异化和顾客满意度均具有显著的正向影响,并在区域创新环境的三个维度对创新产品顾客满意度的影响中有着完全的中介效应,同时在区域政策环境对于创新产品差异化的影响中有着完全的中介效应,而在区域要素环境和文化环境对创新产品差异化的影响中有着部分的中介效应;科技型小微企业的应用性学习仅对创新产品顾客满意度有着显著的正向影响,并在区域要素环境和政策环境对创新产品顾客满意度的影响中有着完全的中介效应。