International Conference on Machine Learning and Computing最新文献_第2页

Collaborative Multi-Auxiliary Information Variational Autoencoder for Recommender Systems 推荐系统的协同多辅助信息变分自编码器

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318336

Jin-Bo Bai, Zhijie Ban

引用次数: 3

Feature Fusion Attention Visual Question Answering 特征融合注意视觉问答

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318305

Chunlin Wang, Jianyong Sun, Xiaolin Chen

引用次数: 1

Machine Learning Techniques for Heart Disease Datasets: A Survey 心脏病数据集的机器学习技术:一项调查

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318343

Younas Khan, Usman Qamar, Nazish Yousaf, Aimal Khan

引用次数: 29

Handling Non-linear Relations in Support Vector Machines through Hyperplane Folding 利用超平面折叠处理支持向量机中的非线性关系

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318319

L. Lundberg, H. Lennerstad, V. Boeva, E. García-Martín

引用次数: 1

Research on Classification of Scientific and Technological Documents Based on Naive Bayes 基于朴素贝叶斯的科技文献分类研究

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318330

Hong Zhang, Hanshuo Wei, Yeye Tang, Qiumei Pu

引用次数: 3

Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features Factoring 基于集合学习的出租公寓价格分类特征因子预测模型

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318377

Asif Ahmed Neloy, H. M. Sadman Haque, Md. Mahmud Ul Islam

{"title":"Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features Factoring","authors":"Asif Ahmed Neloy, H. M. Sadman Haque, Md. Mahmud Ul Islam","doi":"10.1145/3318299.3318377","DOIUrl":"https://doi.org/10.1145/3318299.3318377","url":null,"abstract":"Apartment rental prices are influenced by various factors. The aim of this study is to analyze the different features of an apartment and predict the rental price of it based on multiple factors. An ensemble learning based prediction model is created to reach the goal. We have used a dataset from bProperty.com which includes the rental price and different features of apartments in the city of Dhaka, Bangladesh. The results show the accuracy and prediction of the rent of an apartment, also indicates the different types of categorical values that affect the machine learning models. Another purpose of the study is to find out the factors that signify the apartment rental price in Dhaka. To help our prediction we take on the Advance Regression Techniques (ART) and compare to different features of an apartment for establishing an acceptable model. The following algorithms are selected as the base predictors -- Advance Linear Regression, Neural Network, Random Forest, Support Vector Machine (SVM) and Decision Tree Regressor. The Ensemble learning is stacked of following algorithms -- Ensemble AdaBoosting Regressor, Ensemble Gradient Boosting Regressor, Ensemble XGBoost. Also, Ridge Regression, Lasso Regression, and Elastic Net Regression has been used to combine the advance regression techniques. Tree-based algorithms generate a decision tree from categorical 'YES' and 'NO' values, Ensemble methods to boosting up the learning and prediction accuracy, Support Vector Machine to extend the model for both classification and regression approach and lastly advance linear regression to predict the house price with different features values.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116451397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

A Flexible Approach for Human Activity Recognition Based on Broad Learning System 基于广义学习系统的灵活人体活动识别方法

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318318

Zhidi Lin, Haipeng Chen, Qi Yang, Xuemin Hong

{"title":"A Flexible Approach for Human Activity Recognition Based on Broad Learning System","authors":"Zhidi Lin, Haipeng Chen, Qi Yang, Xuemin Hong","doi":"10.1145/3318299.3318318","DOIUrl":"https://doi.org/10.1145/3318299.3318318","url":null,"abstract":"Deep Learning (DL) based methods have recently been receiving attention in Human Activity Recognition (HAR) for their strong capability of nonlinear mapping. However, these methods suffer from high time consumption during training process due to enormous network parameters. Moreover, the DL-based scheme is less capable of incremental learning which is important for some online human activity recognition applications. In this paper, the Broad Learning System (BLS) known as a promising alternative to DL-based methods is introduced to the classification of human activities. Both the online and offline BLS-based recognition frameworks are proposed to enhance the system flexibility. Specifically, during the online training stage, the artificial hyperspherical data generation model is incorporated into the incremental BLS, enabling it to update the model to accommodate new incoming data more efficiently. Experiments are made towards the proposed BLS network based upon two public human activity datasets, namely, HART and WISDM. The results demonstrate the advantage of the proposed BLS-based scheme over the classic DL-based approaches in terms of the training speed and prediction accuracy.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114774444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Decentralized Adaptive Latency-Aware Cloud-Edge-Dew Architecture for Unreliable Network 不可靠网络的分散自适应延迟感知云边露架构

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318380

Getenet Tefera, Kun She, F. Deeba

引用次数: 7

An Improved Stochastic Linear Cross-entropy Method for Non-convex Economic Dispatch 一种改进的随机线性交叉熵法求解非凸经济调度

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318387

Qun Niu, Yang Zhang, Han Wang

引用次数: 0

Discharge Fault Simulation System for High Voltage SF6 Gas Insulated Switch-gear and Its Intelligent Pattern Recognition 高压SF6气体绝缘开关设备放电故障仿真系统及其智能模式识别

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318334

Shiling Zhang

{"title":"Discharge Fault Simulation System for High Voltage SF6 Gas Insulated Switch-gear and Its Intelligent Pattern Recognition","authors":"Shiling Zhang","doi":"10.1145/3318299.3318334","DOIUrl":"https://doi.org/10.1145/3318299.3318334","url":null,"abstract":"In this paper, the defect simulator for high voltage sulfur hexafluoride gas insulated composite electrical apparatus is developed. The device consists of four parts: sulfur hexafluoride gas chamber, solid insulator, defect simulator, observation and measurement device. The defect simulator can effectively simulate free metal particle discharge, tip discharge, suspension discharge and air gap discharge. A real-type integrated defect simulator based on GIS is developed, and the partial discharge signal is tested on the simulator, the change trend of decomposed gas with time is detected. Based on this, an artificial intelligence classification method combining fuzzy ISODATA algorithm and ant colony algorithm is proposed, and the structure parameters of the two algorithms are optimized by PSO algorithm. The field application results of HV combined electrical appliances show that the proposed method is effective. The fault type diagnosis method can effectively judge the fault mode intelligently according to time series of SF6 micro-decomposition gas and typical micro-decomposition gas. This paper not only collects the original classification data from the hardware platform of the defect simulator, but also develops an artificial intelligence classification algorithm software system which is easy to be programmed. It can be directly and effectively used to diagnose and evaluate the type of insulation defect in the field practical engineering of GIS. It has certain theoretical guidance value for GIS equipment fault diagnosis and pattern recognition.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122024699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0