2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第8页

Deep Learning for Microalgae Classification 微藻分类的深度学习

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-183

Iago Correa, Paulo L. J. Drews-Jr, S. Botelho, M. S. Souza, V. Tavano

引用次数: 32

Integrating Prior Knowledge into Deep Learning 将先验知识融入深度学习

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-37

Michelangelo Diligenti, Soumali Roychowdhury, M. Gori

{"title":"Integrating Prior Knowledge into Deep Learning","authors":"Michelangelo Diligenti, Soumali Roychowdhury, M. Gori","doi":"10.1109/ICMLA.2017.00-37","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-37","url":null,"abstract":"Deep learning allows to develop feature representations and train classification models in a fully integrated way. However, learning deep networks is quite hard and it improves over shallow architectures only if a large number of training data is available. Injecting prior knowledge into the learner is a principled way to reduce the amount of required training data, as the learner does not need to induce the knowledge from the data itself. In this paper we propose a general and principled way to integrate prior knowledge when training deep networks. Semantic Based Regularization (SBR) is used as underlying framework to represent the prior knowledge, expressed as a collection of first-order logic clauses (FOL), and where each task to be learned corresponds to a predicate in the knowledge base. The knowledge base correlates the tasks to be learned and it is translated into a set of constraints which are integrated into the learning process via backpropagation. The experimental results show how the integration of the prior knowledge boosts the accuracy of a state-of-the-art deep network on an image classification task.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"44 1","pages":"920-923"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90462121","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: 68

OP-DCI: A Riskless K-Means Clustering for Influential User Identification in MOOC Forum 基于无风险k -均值聚类的MOOC论坛影响力用户识别

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-34

X. Hou, Chi-Un Lei, Yu-Kwong Kwok

引用次数: 3

An Empirical Study of the Hidden Matrix Rank for Neural Networks with Random Weights 随机权重神经网络隐矩阵秩的实证研究

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-44

Pablo A. Henríquez, G. A. Ruz

引用次数: 4

Relevancy Ranking of User Recommendations of Services Based on Browsing Patterns 基于浏览模式的用户服务推荐相关度排序

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-66

Suresh Kumar Gudla, Joy Bose, Venugopal Gajam, S. Srinivasa

{"title":"Relevancy Ranking of User Recommendations of Services Based on Browsing Patterns","authors":"Suresh Kumar Gudla, Joy Bose, Venugopal Gajam, S. Srinivasa","doi":"10.1109/ICMLA.2017.00-66","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-66","url":null,"abstract":"There are a number of inbound web services, which recommend content to users. However, there is no way for such services to prioritize their recommendations as per the users' interests. Here we are not interested in generating new recommendations, but rather organizing and prioritizing existing recommendations in order to increase the click rate. Since users have different patterns of browsing that also change frequently, it is good to have a system that prioritizes recommendations based on the current browsing patterns of individual users. In this paper we present such a system. We first generate the clusters of article topics using URLs from the users' browsing history, which is then used to generate the relevancy scores of the recommendation services based on entropy. The relevancy scores are then fed to the service providers, which use them to prioritize their recommendations by ranking them based on the relevancy scores. We test the model using the browsing history for 10 users, and validate the model by calculating the correlation of the generated relevancy scores with the users' manually provided topic preferences. We further use collaborative filtering to benchmark the usefulness of our ranking systems.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"31 1","pages":"765-768"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76689519","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: 4

NMF-Based Label Space Factorization for Multi-label Classification 基于nmf的标签空间分解多标签分类

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-144

Mohammad Firouzi, Mahmood Karimian, Mahdieh Soleymani

引用次数: 1

An Investigation of How Neural Networks Learn from the Experiences of Peers Through Periodic Weight Averaging 神经网络通过周期加权平均学习同伴经验的研究

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-72

Joshua Smith, Michael S. Gashler

引用次数: 2

Early Prediction of College Attrition Using Data Mining 基于数据挖掘的高校人员流失早期预测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.000-6

L. C. B. Martins, Rommel N. Carvalho, Ricardo Silva Carvalho, M. Victorino, M. Holanda

引用次数: 28

RobustSPAM for Inference from Noisy Longitudinal Data and Preservation of Privacy 基于噪声纵向数据推断和隐私保护的鲁棒垃圾邮件

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-137

Anna Palczewska, Jan Palczewski, G. Aivaliotis, Lukasz Kowalik

引用次数: 3

Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life 预测模型策略以了解生命早期哮喘的异质表现

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-176

D. Belgrave, R. Cassidy, D. Stamate, A. Custovic, L. Fleming, A. Bush, S. Saglani

{"title":"Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life","authors":"D. Belgrave, R. Cassidy, D. Stamate, A. Custovic, L. Fleming, A. Bush, S. Saglani","doi":"10.1109/ICMLA.2017.0-176","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-176","url":null,"abstract":"Wheezing is common among children and ∼50% of those under 6 years of age are thought to experience at least one episode of wheeze. However, due to the heterogeneity of symptoms there are difficulties in treating and diagnosing these children. ‘Phenotype specific therapy’ is one possible avenue of treatment, whereby we use significant pathology and physiology to identify and treat pre-schoolers with wheeze. By performing feature selection algorithms and predictive modelling techniques, this study will attempt to determine if it is possible to robustly distinguish patient diagnostic categories among pre-school children. Univariate feature analysis identified more objective variables and recursive feature elimination a larger number of subjective variables as important in distinguishing between patient categories. Predicative modelling saw a drop in performance when subjective variables were removed from analysis, indicating that these variables are important in distinguishing wheeze classes. We achieved 90%+ performance in AUC, sensitivity, specificity, and accuracy, and 80%+ in kappa statistic, in distinguishing ill from healthy patients. Developed in a synergistic statistical - machine learning approach, our methodologies propose also a novel ROC Cross Evaluation method for model post-processing and evaluation. Our predictive modelling's stability was assessed in computationally intensive Monte Carlo simulations.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"68-75"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76564511","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: 8