2010 Ninth International Conference on Machine Learning and Applications最新文献_第2页

Kernel-based Approaches for Collaborative Filtering 基于核的协同过滤方法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.41

Zhonghang Xia, Wenke Zhang, Manghui Tu, I. Yen

引用次数: 1

Public Goods Game Simulator with Reinforcement Learning Agents 具有强化学习代理的公共物品博弈模拟器

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.14

U. ManChon, Z. Li

引用次数: 6

Intelligent Classification System Using a Pruned Bayes Fuzzy Rule Set 基于修剪贝叶斯模糊规则集的智能分类系统

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.98

I. Yin, Estevam Hruschka, H. Camargo

{"title":"Intelligent Classification System Using a Pruned Bayes Fuzzy Rule Set","authors":"I. Yin, Estevam Hruschka, H. Camargo","doi":"10.1109/ICMLA.2010.98","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.98","url":null,"abstract":"Hybrid intelligent systems which take advantage of the Bayesian/Fuzzy collaboration have been explored in the literature in the last years. Such collaboration can play an important role mainly in real intelligent systems applications, where accuracy and comprehensibility are crucial aspects to be considered. This paper further explore the Bayes Fuzzy method proposing a classification method specially designed to be used in intelligent systems for data analysis. The main idea is to enhance comprehensibility while maintaining accuracy by decreasing the number of fuzzy rules used to explain a Bayesian Classifier (BC). The proposed Pruned Bayes Fuzzy 2 (PBF2) method is based on a new feature selection method named Selection by Markov Blanket Relation Strength (SMBRS). In the performed experiments, PBF2 is empirically applied to a real world police records problem in order to extract a comprehensible and accurate set of rules which can help in crime prevention. The obtained results show PBF2, when used with proper parameters, brings better precision and comprehensibility compared to other Bayesian/Fuzzy-based methods and to C4.5 algorithm.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132592279","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

Aggregating Multiple Biological Measurements Per Patient 汇总每位患者的多项生物学测量

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.120

V. Zubek, F. Khan

引用次数: 2

A Binocular Framework for Face Liveness Verification under Unconstrained Localization 无约束定位下的双目人脸活动性验证框架

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.37

Qi Li, Zhonghang Xia, Guangming Xing

引用次数: 3

Spatial Based Feature Generation for Machine Learning Based Optimization Compilation 基于空间特征生成的机器学习优化编译

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.147

A. Malik

{"title":"Spatial Based Feature Generation for Machine Learning Based Optimization Compilation","authors":"A. Malik","doi":"10.1109/ICMLA.2010.147","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.147","url":null,"abstract":"Modern compilers provide optimization options to obtain better performance for a given program. Effective selection of optimization options is a challenging task. Recent work has shown that machine learning can be used to select the best compiler optimization options for a given program. Machine learning techniques rely upon selecting features which represent a program in the best way. The quality of these features is critical to the performance of machine learning techniques. Previous work on feature selection for program representation is based on code size, mostly executed parts, parallelism and memory access patterns with-in a program. Spatial based information–how instructions are distributed with-in a program–has never been studied to generate features for the best compiler options selection using machine learning techniques. In this paper, we present a framework that address how to capture the spatial information with-in a program and transform it to features for machine learning techniques. An extensive experimentation is done using the SPEC2006 and MiBench benchmark applications. We compare our work with the IBM Milepost-gcc framework. The Milepost work gives a comprehensive set of features for using machine learning techniques for the best compiler options selection problem. Results show that the performance of machine learning techniques using spatial based features is better than the performance using the Milepost framework. With 66 available compiler options, we are also able to achieve 70% of the potential speed up obtained through an iterative compilation.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117231964","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: 13

Peptide Sequence Tag-Based Blind Identification-based SVM Model 基于肽序列标签的盲识别SVM模型

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.156

Hui Li, Chunmei Liu, Xumin Liu, M. Diakite, L. Burge, A. Yakubu, W. Southerland

引用次数: 0

Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets 基于深度信念网的脑电信号半监督异常检测

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.71

Drausin Wulsin, Justin A. Blanco, R. Mani, B. Litt

{"title":"Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets","authors":"Drausin Wulsin, Justin A. Blanco, R. Mani, B. Litt","doi":"10.1109/ICMLA.2010.71","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.71","url":null,"abstract":"Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as signatures of abnormal brain. These pathologic EEG waveforms, once detected, often necessitate accute clinincal interventions, but these events are typically rare, highly variable between patients, and often hard to separate from background, making them difficult to reliably detect. We show that Deep Belief Nets (DBNs), a type of multi-layer generative neural network, can be used effectively for such EEG anomaly detection. We compare this technique to the state-of-the-art, a one-class Support Vector Machine (SVM), showing that the DBN outperforms the SVM by the F1 measure for our EEG dataset. We also show how the outputs of a DBN-based detector can be used to aid visualization of anomalies in large EEG data sets and propose a method for using DBNs to gain insight into which features of signals are characteristically anomalous. These findings show that Deep Belief Nets can facilitate human review of large amounts of clinical EEG as well as mining new EEG features that may be indicators of unusual activity.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127475327","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: 86

On the Scalability of Supervised Learners in Metagenomics 元基因组学中监督学习器的可扩展性研究

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.123

U. ManChon, Vasim Mahamuda, K. Rasheed

引用次数: 0

Power Iteration Denoising 幂次迭代去噪

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.131

Panganai Gomo, Mike Spann

引用次数: 0