International Conference on Machine Learning and Computing最新文献_第8页

Research on the Application of Big Data Management in Enterprise Management Decision-making and Execution Literature Review 大数据管理在企业管理决策与执行中的应用研究文献综述

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318388

Zhiyi Zhuo, Shanhu Zhang

引用次数: 1

Image Captioning Based on Automatic Constraint Loss 基于自动约束损失的图像字幕

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318375

Chaoqian Xu, G. Zhu, Lixin Wang

引用次数: 1

Vehicle Detection Based on Drone Images with the Improved Faster R-CNN 基于改进更快R-CNN的无人机图像车辆检测

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318383

Lixin Wang, Junguo Liao, Chaoqian Xu

引用次数: 18

Model Loss and Distribution Analysis of Regression Problems in Machine Learning 机器学习中回归问题的模型损失与分布分析

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318367

Nan Yang, Zeyu Zheng, Tianran Wang

{"title":"Model Loss and Distribution Analysis of Regression Problems in Machine Learning","authors":"Nan Yang, Zeyu Zheng, Tianran Wang","doi":"10.1145/3318299.3318367","DOIUrl":"https://doi.org/10.1145/3318299.3318367","url":null,"abstract":"The machine learning regression model is based on the assumption of normal distribution. In this paper, we mainly study the probability distribution of the machine learning model and the effect of the convergence values of different loss functions on the probability distribution model. Based on the idea of robust regression and the assumption of homogeneous variance of the model, we solved the statistical solution of two-dimensional regression problem by using least square method. The maximum likelihood estimation parameters of the probabilistic model are obtained by using the maximum likelihood estimation method. In order to compare the solving parameters of the two methods, the convergence values of L1 loss function and L2 loss function are used for the regression verification. Through the mathematical and statistical rigorous derivation, obtained two important conclusions; First, under the condition that the data satisfies normal distribution and is based on the assumption of homogeneous variance, the probability model conforms to the multivariate gaussian distribution. Secondly, the model satisfying the multi-gaussian distribution has little influence on the parameter estimation under the condition of the large number theorem, that is, the multi-gaussian distribution model has good tolerance to the loss function.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"33 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":"116825324","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: 7

An Improved Face Synthesis Model for Two-Pathway Generative Adversarial Network 一种改进的双路径生成对抗网络人脸综合模型

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318346

Changlin Li, Zhangjin Huang

引用次数: 1

Research on the Periodical Behavior Discovery of Funds in Anti-money Laundering Investigation 反洗钱调查中资金周期性行为发现研究

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318356

Shiliang He, Zhenxin Qu

引用次数: 1

Asymptotic Stability of Nonlinear Impulsive Stochastic Systems with Markovian Switching 具有马尔可夫切换的非线性脉冲随机系统的渐近稳定性

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318304

Xinwen Zhang, Chao Jia, Weiguo Liu

引用次数: 0

A General Multi-Source Data Fusion Framework 通用多源数据融合框架

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318394

Wei-Ming Liu, Chen Zhang, Bin Yu, Yitong Li

引用次数: 4

Visual Optimization of Cluster Simulation Based on Multi Process Service and Load Balancing Agent 基于多进程服务和负载均衡代理的集群仿真可视化优化

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318306

Y. Xiao, Mei-Min Wu, Qian Bi

引用次数: 0

A Hybrid Model of Least Squares Support Vector Regression Optimized by Particle Swarm Optimization for Electricity Demand Prediction 基于粒子群优化的最小二乘支持向量回归混合模型用于电力需求预测

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318332

Zirong Li, Lian Li

{"title":"A Hybrid Model of Least Squares Support Vector Regression Optimized by Particle Swarm Optimization for Electricity Demand Prediction","authors":"Zirong Li, Lian Li","doi":"10.1145/3318299.3318332","DOIUrl":"https://doi.org/10.1145/3318299.3318332","url":null,"abstract":"To further increase prediction accuracy, improve power management and reduce waste, this paper proposes a hybrid electric load forecasting model based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with particle swarm optimization (PSO) algorithm. Where wavelet analysis is used to transform the original electric data sequence into multi-resolution subsets during the preprocessing stage and then the decomposed subsets are inserted into LSSVR to realize prediction, finally the ultimate prediction results are obtained via the wavelet reconstruction with all the independent prediction results. However, the key to influence forecasting accuracy is the parameters used in the LSSVR, in this paper PSO is used to optimize the kernel parameter Δ and the regularization parameter γ of LSSVR and choose the appropriate parameters for the hybrid forecasting model. The effectiveness of the proposed hybrid model has been proved in electric load prediction; the prediction results show that the proposed hybrid model outperforms the Elman networks model, the radial basis function (RBF) neural network model and LSSVR optimized only with PSO. The hybrid model achieves satisfying results, the mean absolute percentage error (MAPE) with 0.907% and the coefficient of determination (R 2) with 0.9936, it offers a higher forecasting precision.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"41 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":"121258390","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: 2