2010 Ninth International Conference on Machine Learning and Applications最新文献_第10页

Speeding Up Greedy Forward Selection for Regularized Least-Squares 加速正则化最小二乘的贪婪正向选择

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.55

T. Pahikkala, A. Airola, T. Salakoski

引用次数: 24

A Parallel Algorithm for Predicting the Secondary Structure of Polycistronic MicroRNAs 预测多顺反子microrna二级结构的并行算法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.80

Dianwei Han, G. Tang, Jun Zhang

{"title":"A Parallel Algorithm for Predicting the Secondary Structure of Polycistronic MicroRNAs","authors":"Dianwei Han, G. Tang, Jun Zhang","doi":"10.1109/ICMLA.2010.80","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.80","url":null,"abstract":"MicroRNAs (miRNAs) are newly discovered endogenous small non-coding RNAs (21-25nt) that target their complementary gene transcripts for degradation or translational repression. The biogenesis of a functional miRNA is largely dependent on the secondary structure of the miRNA precursor (pre-miRNA). Recently, it has been shown that miRNAs are present in the genome as the form of polycistronic transcriptional units in plants and animals. It will be important to design methods to predict such structures for miRNA discovery and its applications in gene silencing. In this paper, we propose a parallel algorithm based on the master-slave architecture to predict the secondary structure from an input sequence. First, the master processor partitions the input sequence into subsequences and distributes them to the slave processors. The slave processors will then predict the secondary structure based on their individual task. Afterward, the slave processors will return their results to the master processor. Finally, the master processor will merge the partial structures from the slave processors into a whole candidate secondary structure. The optimal structure is obtained by sorting the candidate structures according to their scores. Our experimental results indicate that the actual speed-ups match the trend of theoretic values.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"1 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":"132600453","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

Extreme Volume Detection for Managed Print Services 托管打印服务的极限体积检测

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.95

J. Handley, Marie-Luise Schneider, Victor Ciriza, J. Earl

{"title":"Extreme Volume Detection for Managed Print Services","authors":"J. Handley, Marie-Luise Schneider, Victor Ciriza, J. Earl","doi":"10.1109/ICMLA.2010.95","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.95","url":null,"abstract":"A managed print service (MPS) manages the printing, scanning and facsimile devices in an enterprise to control cost and improve availability. Services include supplies replenishment, maintenance, repair, and use reporting. Customers are billed per page printed. Data are collected from a network of devices to facilitate management. The number of pages printed per device must be accurately counted to fairly bill the customer. Software errors, hardware changes, repairs, and human error all contribute to “meter reads” that are exceptionally high and are apt to be challenged by the customer were they to be billed. Account managers periodically review data for each device in an account. This process is tedious and time consuming and an automated solution is desired. Exceptional print volumes are not always salient and detecting them statistically is prone to errors owing to nonstationarity of the data. Mean levels and variances change over time and usage is highly auto correlated which precludes simple detection methods based on deviations from an average background. A solution must also be computationally inexpensive and require little auxiliary storage because hundreds of thousands of streams of device data must be processed. We present an algorithm and system for online detection of extreme print volumes that uses dynamic linear models (DLM) with variance learning. A DLM is a state space time series model comprising a random mean level system process and a random observation process. Both components are updated using Bayesian statistics. After each update, a forecasted value and its estimated variance are calculated. A read is flagged as exceptionally high if its value is highly unlikely with respect to a forecasted value and its standard deviation. We provide implementation details and results of a field test in which error rate was decreased from 26.4% to 0.5% on 728 observed meter reads.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"212 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":"133610229","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

Improved Unsupervised Clustering over Watershed-Based Clustering 基于分水岭聚类的改进无监督聚类

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.44

Sai Venu Gopal Lolla, L. L. Hoberock

引用次数: 2

Parallel Projections for Manifold Learning 流形学习的并行投影

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.54

H. Strange, R. Zwiggelaar

引用次数: 1

Bayesian Inferences and Forecasting in Spatial Time Series Models 空间时间序列模型中的贝叶斯推断与预测

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.170

Sung Duck Lee, Duck-Ki Kim

引用次数: 1

Multilayer Ferns: A Learning-based Approach of Patch Recognition and Homography Extraction 多层蕨类植物:基于学习的斑块识别和单应性提取方法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.36

Gao Ce, Song Yixu, Jia Pei-fa

引用次数: 2

Patient-Specific Seizure Detection from Intra-cranial EEG Using High Dimensional Clustering 基于高维聚类的颅内脑电图患者特异性癫痫检测

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.119

Haimonti Dutta, D. Waltz, Karthik M. Ramasamy, Philip Gross, Ansaf Salleb-Aouissi, H. Diab, Manoj Pooleery, C. Schevon, R. Emerson

{"title":"Patient-Specific Seizure Detection from Intra-cranial EEG Using High Dimensional Clustering","authors":"Haimonti Dutta, D. Waltz, Karthik M. Ramasamy, Philip Gross, Ansaf Salleb-Aouissi, H. Diab, Manoj Pooleery, C. Schevon, R. Emerson","doi":"10.1109/ICMLA.2010.119","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.119","url":null,"abstract":"Automatic seizure detection is becoming popular in modern epilepsy monitoring units since it assists diagnostic monitoring and reduces manual review of large volumes of EEG recordings. In this paper, we describe the application of machine learning algorithms for building patient-specific seizure detectors on multiple frequency bands of intra-cranial electroencephalogram (iEEG) recorded by a dense Micro-Electrode Array (MEA). The MEA is capable of recording at a very high sampling rate (30 KHz) producing an avalanche of time series data. We explore subsets of this data to build seizure detectors – we discuss several methods for extracting univariate and bivariate features from the channels and study the effectiveness of using high dimensional clustering algorithms such as K-means and Subspace clustering for constructing the model. Future work involves design of more robust seizure detectors using other features and non-parametric clustering techniques, detection of artifacts and understanding the generalization properties of the models.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"1 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":"125448492","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: 3

A Bayesian Nonparametric Model for Joint Relation Integration and Domain Clustering 联合关系集成与域聚类的贝叶斯非参数模型

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.168

Dazhuo Li, Fahim Mohammad, E. Rouchka

引用次数: 1

Multi-Agent Inverse Reinforcement Learning 多智能体逆强化学习

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.65

Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, K. Kersting, J. Shavlik

引用次数: 77