2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)最新文献_第9页

An Application of Neural Networks to Predicting Mastery of Learning Outcomes in the Treatment of Autism Spectrum Disorder 神经网络在自闭症谱系障碍治疗中预测学习结果掌握的应用

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.214

Erik J. Linstead, Rene German, Dennis R. Dixon, D. Granpeesheh, Marlena N. Novack, Alva Powell

引用次数: 15

Application of a Multilayer Perceptron Neural Network for Classifying Software Platforms of a Powered Prosthesis through a Force Plate 多层感知器神经网络在力板助力假肢软件平台分类中的应用

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.211

R. LeMoyne, Timothy Mastroianni, A. Hessel, K. Nishikawa

引用次数: 7

Incremental Learning on Decorrelated Approximators 去相关逼近器的增量学习

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.153

Jan H. Schoenke, W. Brockmann

{"title":"Incremental Learning on Decorrelated Approximators","authors":"Jan H. Schoenke, W. Brockmann","doi":"10.1109/ICMLA.2015.153","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.153","url":null,"abstract":"In general, designing an incremental learning system for a particular task at least consists of choosing an appropriate approximation structure and learning algorithm. Common Linear In the Parameters (LIP) approximation structures are for example polynomials, radial basis functions or grid-based lookup tables. Typical learning algorithms accompanying them are for example Passive-Aggressive (PA) or Recursive Least Squares (RLS). Usually, these two choices are not independent as not every learning algorithm is able to handle any approximation structure well. Here we present a formalism that allows the designer to treat these two design aspects independently from each other. By decorrelating the basis functions of the approximator we form a new set of basis functions that can be handled by any learning algorithm. We develop design guidelines in order to make our approach an easy to use tool and to support the designer in making the learning progress reliable at design time. Further, we look at the properties of our approach as an extension to LIP approximators and investigate its implications for the behavior of the incremental learning system using artificial, benchmark and real world data sets for regression tasks.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132860066","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

Automatically Discovering Fatigue Patterns from Sparsely Labelled Temporal Data 从稀疏标记的时间数据中自动发现疲劳模式

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.51

Karen Guo, Paul Schrater

引用次数: 0

A Web-Based Auction Platform for Electricity Retail Markets 基于网络的电力零售市场拍卖平台

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.17

Burak Colak, M. Gokmen, H. Kiliç

引用次数: 2

A Support Vector Classification Model with Partial Empirical Risks Given 给出了一种具有部分经验风险的支持向量分类模型

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.45

Linkai Luo, Ling-Jun Ye, Qifeng Zhou, Hong Peng

引用次数: 0

Local Coordinate Projective Non-negative Matrix Factorization 局部坐标投影非负矩阵分解

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.47

Qing Liao, Xiang Zhang, Naiyang Guan, Qian Zhang

引用次数: 0

Recognizing Human Activities from Raw Accelerometer Data Using Deep Neural Networks 利用深度神经网络从原始加速度计数据中识别人类活动

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.48

Licheng Zhang, Xihong Wu, D. Luo

{"title":"Recognizing Human Activities from Raw Accelerometer Data Using Deep Neural Networks","authors":"Licheng Zhang, Xihong Wu, D. Luo","doi":"10.1109/ICMLA.2015.48","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.48","url":null,"abstract":"Activity recognition from wearable sensor data has been researched for many years. Previous works usually extracted features manually, which were hand-designed by the researchers, and then were fed into the classifiers as the inputs. Due to the blindness of manually extracted features, it was hard to choose suitable features for the specific classification task. Besides, this heuristic method for feature extraction could not generalize across different application domains, because different application domains needed to extract different features for classification. There was also work that used auto-encoders to learn features automatically and then fed the features into the K-nearest neighbor classifier. However, these features were learned in an unsupervised manner without using the information of the labels, thus might not be related to the specific classification task. In this paper, we recommend deep neural networks (DNNs) for activity recognition, which can automatically learn suitable features. DNNs overcome the blindness of hand-designed features and make use of the precious label information to improve activity recognition performance. We did experiments on three publicly available datasets for activity recognition and compared deep neural networks with traditional methods, including those that extracted features manually and auto-encoders followed by a K-nearest neighbor classifier. The results showed that deep neural networks could generalize across different application domains and got higher accuracy than traditional methods.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123808200","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: 44

Efficient and Rotation Invariant Fingerprint Matching Algorithm Using Adjustment Factor 基于调整因子的高效旋转不变性指纹匹配算法

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.226

Asif Iqbal Khan, M. Wani

引用次数: 5

Multi-period Prediction of Solar Radiation Using ARMA and ARIMA Models 利用ARMA和ARIMA模式对太阳辐射进行多周期预测

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.33

I. Colak, M. Yesilbudak, N. Genç, R. Bayindir

引用次数: 54