2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第7页

Bayesian Networks for Inverse Inference in Manufacturing Bayesian Networks 制造贝叶斯网络中的逆推理贝叶斯网络

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-91

Avadhut Sardeshmukh, S. Reddy, BP Gautham, Amol Joshi

引用次数: 3

Bias Discovery in News Articles Using Word Vectors 使用词向量在新闻文章中发现偏见

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-62

A. Patankar, Joy Bose

引用次数: 5

An Evolutionary Approach to General-Purpose Automated Speed and Lane Change Behavior 通用自动变速和变道行为的进化方法

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-70

C. Hoel, M. Wahde, Krister Wolff

引用次数: 4

Schemes for Labeling Semantic Code Clones using Machine Learning 使用机器学习标记语义代码克隆的方案

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-25

Abdullah M. Sheneamer, H. Hazazi, Swarup Roy, J. Kalita

引用次数: 7

Evaluating Non-personalized Single-Heuristic Active Learning Strategies for Collaborative Filtering Recommender Systems 协同过滤推荐系统非个性化单启发式主动学习策略评估

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-96

Georges Chaaya, Elisabeth Métais, J. B. Abdo, Raja Chiky, J. Demerjian, K. Barbar

{"title":"Evaluating Non-personalized Single-Heuristic Active Learning Strategies for Collaborative Filtering Recommender Systems","authors":"Georges Chaaya, Elisabeth Métais, J. B. Abdo, Raja Chiky, J. Demerjian, K. Barbar","doi":"10.1109/ICMLA.2017.00-96","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-96","url":null,"abstract":"In collaborative filtering recommender systems, the users rate items, and this process helps in understanding their preferences. The systems can suffer from the cold-start problem, which refers to the absence or insufficiency of ratings for new users. This can be solved by using active learning strategies, which can be non-personalized or personalized, and which were evaluated and tested previously using different datasets and metrics. In this paper, we present a clearer study by implementing the main non-personalized single-heuristic strategies (random, popularity, co—coverage, variance, entropy, entropy0) on the same dataset, and by evaluating them using the same metrics, in order to have a better comparison. We use the public MovieLens dataset in the experimentations and the results show that the random strategy performs the worst, whereas the entropy0 leads to the best results. All strategies except the random strategy lead to very close results at a certain point, where ratings for almost the same items will have been elicited.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"3 1","pages":"593-600"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89702996","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: 10

Machine Learning Methods Used in Evaluations of Secure Biometric System Components 用于安全生物识别系统组件评估的机器学习方法

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-120

Bilgehan Arslan, Mehtap Ülker, Ş. Sağiroğlu

引用次数: 3

Classification-Based Adaptive Web Scraper 基于分类的自适应Web Scraper

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-168

S. UjwalB.V., Bharat Gaind, Abhishek Kundu, Anusha K. Holla, Mukund Rungta

引用次数: 9

Surface Roughness Discrimination Using Unsupervised Machine Learning Algorithms 使用无监督机器学习算法的表面粗糙度判别

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-49

Longhui Qin, Yilei Zhang

引用次数: 1

Modeling a Classifier for Solving Safety-Critical Binary Classification Tasks 为解决安全关键型二元分类任务建模分类器

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-38

Ibrahim Alagöz, Thomas Hoiss, R. German

{"title":"Modeling a Classifier for Solving Safety-Critical Binary Classification Tasks","authors":"Ibrahim Alagöz, Thomas Hoiss, R. German","doi":"10.1109/ICMLA.2017.00-38","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-38","url":null,"abstract":"This paper introduces a novel machine learning approach for performing binary decision-making tasks under uncertainty. Reducing the regression test effort of safety-critical black box systems is a safety-critical task as system failures would remain undetected if corresponding failing test cases are not executed. The uncertainty of potentially undetected system failures persists due to the lack of implementation knowledge of black-box systems. We refer to executing test cases as a costly labeling process due to required special test equipment and testing time. However, we model a binary classifier for selecting test cases. Accordingly, only fault revealing test cases should be selected and thus executed in order to reduce the overall cost of the regression test effort. On the one side, the classifier's specificity has to be maximized. On the other side, the classifier's sensitivity has to meet a specific quality-level since the number of undetected system failures should be limited. We will show in an industrial case study the benefits of our classifier where we reduce the regression test effort of safety-critical systems. The experimental results indicate that our implemented classifier outperforms other machine learning approaches in terms of sensitivity.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"116 1","pages":"914-919"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75998379","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: 1

Attribute Assisted Interpretation Confidence Classification Using Machine Learning 使用机器学习的属性辅助解释置信度分类

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-178

W. Weinzierl

{"title":"Attribute Assisted Interpretation Confidence Classification Using Machine Learning","authors":"W. Weinzierl","doi":"10.1109/ICMLA.2017.0-178","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-178","url":null,"abstract":"An attribute assisted classification deriving estimates of interpretation confidence was performed. Instantaneous and coherency attributes were used in a supervised followed by an unsupervised classification resulting in an error envelope of the interpretation. In an initial approximation, confidence weights for a signal and background response are estimated using support vector machine learning. Subsequently, a weighted discrimination based on several coherency attributes using self-organizing maps is obtained. The resulting quantization is used as additional input and constraint in a final probability assessment of signal confidence using instantaneous attributes in support vector machine learning. The additional input in the form of quantization vectors and possible reduction in dimensionality of the input attribute vector space, allows to combine highly non-linear correlations in a multivariate discrimination. The trained classification is used to assign signal confidence probabilities to an interpreted seismic horizon. The proposed methodology is applied to an onshore data set from Wyoming, USA, revealing how single- and multi-trace attributes can be used to quantitatively assess the uncertainty of an interpretation often lost during project maturation.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"11 1","pages":"55-60"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90728593","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: 1