2011 10th International Conference on Machine Learning and Applications and Workshops最新文献

{"title":"Simple Reinforcement Learning for Small-Memory Agent","authors":"A. Notsu, Katsuhiro Honda, H. Ichihashi, Yuki Komori","doi":"10.1109/ICMLA.2011.127","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.127","url":null,"abstract":"In this paper, we propose Simple Reinforcement Learning for a reinforcement learning agent that has small memory. In the real world, learning is difficult because there are an infinite number of states and actions that need a large number of stored memories and learning times. To solve a problem, estimated values are categorized as ``GOOD\" or ``NO GOOD\" in the reinforcement learning process. Additionally, the alignment sequence of estimated values is changed because they are regarded as an important sequence themselves. We conducted some simulations and observed the influence of our methods. Several simulation results show no bad influence on learning speed.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124453787","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}

{"title":"Comparison of Two Methods for Finding Biomedical Categories in Medline","authors":"Lana Yeganova, Won Kim, Donald C. Comeau, W. Wilbur","doi":"10.1109/ICMLA.2011.50","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.50","url":null,"abstract":"In this paper we describe and compare two methods for automatically learning meaningful biomedical categories in Medline®. The first approach is a simple statistical method that uses part-of-speech and frequency information to extract a list of frequent headwords from noun phrases in Medline. The second method implements an alignment-based technique to learn frequent generic patterns that indicate a hyponymy/hypernymy relationship between a pair of noun phrases. We then apply these patterns to Medline to collect frequent hypernyms, potential biomedical categories. We study and compare these two alternative sets of terms to identify semantic categories in Medline. Our method is completely data-driven.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126311965","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}

{"title":"Combining Corpus-Based Features for Selecting Best Natural Language Sentences","authors":"Foaad Khosmood, R. Levinson","doi":"10.1109/ICMLA.2011.170","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.170","url":null,"abstract":"Automated paraphrasing of natural language text has many interesting applications from aiding in better translations to generating better and more appropriate style language. In this paper, we are concerned with the problem of picking the best English sentence out of a set of machine generated paraphrase sentences, each designed to express the same content as a human generated original. We present a system of scoring sentences based on examples in large corpora. Specifically, we use the Microsoft Web N-Gram service and the text of the Brown Corpus to extract features from all candidate sentences and compare them against each other. We consider three feature combination methods: A handcrafted decision tree, linear regression and linear powerset regression. We find that while each method has particular strengths, the linear power set regression performs best against our human-evaluated test data.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122156914","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}

{"title":"Energy Efficiency for Unmanned Aerial Vehicles","authors":"Balemir Uragun","doi":"10.1109/ICMLA.2011.159","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.159","url":null,"abstract":"This paper emphasizes the energy efficiency issue for unmanned aerial vehicles (UAVs). The power requirement for an UAV system was modeled with the aid of energy requiring from all possible sub-systems. In this model, a single UAV system was broken down by the six power consumption components. The scientific research areas and emerging technologies assisted UAV design stages involved in the mainly \"six-part\" load components; those are (1) Control, (2) Data processing, (3) Communication, (4) Payloads, including sensors with actuators, (5) External Loads as system perturbation, and (6) System Dynamicity with a performance criteria.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133024936","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}

{"title":"The Combination of Clinical, Dose-Related and Imaging Features Helps Predict Radiation-Induced Normal-Tissue Toxicity in Lung-cancer Patients -- An in-silico Trial Using Machine Learning Techniques","authors":"G. Nalbantov, A. Dekker, D. Ruysscher, P. Lambin, E. Smirnov","doi":"10.1109/ICMLA.2011.139","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.139","url":null,"abstract":"The amount of delivered radiation dose to the tumor in non-small cell lung cancer (NSCLC) patients is limited by the negative side effects on normal tissues. The most dose-limiting factor in radiotherapy is the radiation-induced lung toxicity (RILT). RILT is generally measured semi-quantitatively, by a dyspnea, or shortness-of-breath, score. In general, about 20-30% of patients develop RILT several months after treatment, and in about 70% of the patients the delivered dose is insufficient to control the tumor growth. Ideally, if the RILT score would be known in advance, then the dose treatment plan for the low-toxicity-risk patients could be adjusted so that higher dose is delivered to the tumor to better control it. A number of possible predictors of RILT have been proposed in the literature, including dose-related and clinical/demographic patient characteristics available prior to radiotherapy. In addition, the use of imaging features -- which are noninvasive in nature - has been gaining momentum. Thus, anatomic as well as functional/metabolic information from CT and PET scanner images respectively are used in daily clinical practice, which provide further information about the status of a patient. In this study we assessed whether machine learning techniques can successfully be applied to predict post-radiation lung damage, proxied by dyspnea score, based on clinical, dose-related (dosimetric) and image features. Our dataset included 78 NSCLC patients. The patients were divided into two groups: no-deterioration-of-dyspnea, and deterioration-of-dyspnea patients. Several machine-learning binary classifiers were applied to discriminate the two groups. The results, evaluated using the area under the ROC curve in a cross-validation procedure, are highly promising. This outcome could open the possibility to deliver better, individualized dose-treatment plans for lung cancer patients and help the overall clinical decision making (treatment) process.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133517809","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}

{"title":"An Intelligent Decision Support Tool for a Travelling Wave Ultrasonic Motor Based on k-Nearest Neighbor Algorithm","authors":"Ş. Sağiroğlu, H. Kahraman, M. Yesilbudak, I. Colak","doi":"10.1109/ICMLA.2011.33","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.33","url":null,"abstract":"Driving frequency, amplitude and phase difference of two-phase sinusoidal voltages are the input parameters which have influence on speed stability of travelling wave ultrasonic motors (TWUSMs).These parameters are also time-varying due to the variations in operating temperature. In addition, a complete mathematical model of the TWUSM has not been derived yet. Owing to these reasons, a machine learning approach is required for determining the compatibility of operating parameters related to speed stability of TWUSMs. For this purpose, an intelligent decision support tool has been designed for TWUSMs in this study. The input parameters such as driving frequency, amplitude, phase difference of two-phase sinusoidal voltages and operating temperature were evaluated by the k-nearest neighbor algorithm in the decision support tool. The results have shown that the proposed tool provides effective results in the compatibility determination of operating parameters related to speed stability of TWUSMs.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758585","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}

{"title":"Predicting \"Essential\" Genes across Microbial Genomes: A Machine Learning Approach","authors":"Krishna Palaniappan, Sumitra Mukherjee","doi":"10.1109/ICMLA.2011.114","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.114","url":null,"abstract":"Essential genes constitute the minimal set of genes an organism needs for its survival. Identification of essential genes is of theoretical interest to genome biologist and has practical applications in medicine and biotechnology. This paper presents and evaluates machine learning approaches to the problem of predicting essential genes in microbial genomes using solely sequence derived input features. We investigate three different supervised classification methods -- Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT) -- for this binary classification task. The classifiers are trained and evaluated using 37830 examples obtained from 14 experimentally validated, taxonomically diverse microbial genomes whose essential genes are known. A set of 52 relevant genomic sequence derived features is used as input for the classifiers. The models were evaluated using novel blind testing schemes Leave-One-Genome-Out (LOGO) and Leave-One-Taxon-group-Out (LOTO) and 10-fold stratified cross validation (10-f-cv) strategy on both the full multi-genome datasets and its class imbalance reduced variants. Experimental results (10 X 10-f-cv) indicate SVM and ANN perform better than DT with Area under the Receiver Operating Characteristics (AU-ROC) scores of 0.80, 0.79 and 0.68 respectively. This study demonstrates that supervised machine learning methods can be used to predict essential genes in microbial genomes by using only gene sequence and features derived from it. LOGO and LOTO Blind test results suggest that the trained classifiers generalize across genomes and taxonomic boundaries.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124198689","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}

{"title":"An Intelligent Power Factor Correction Approach Based on Linear Regression and Ridge Regression Methods","authors":"R. Bayindir, Murat Gök, E. Kabalci, O. Kaplan","doi":"10.1109/ICMLA.2011.34","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.34","url":null,"abstract":"This study introduces an intelligent power factor correction approach based on Linear Regression (LR) and Ridge Regression (RR) methods. The 10-fold Cross Validation (CV) test protocol has been used to evaluate the performance. The best test performance has been obtained from the LR in comparison with RR. The empirical results have evaluated that the selected intelligent compensators developed in this work might overcome the problems met in the literature providing accurate, simple and low-cost solution for compensation.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116930211","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}

{"title":"Document Clustering for Forensic Computing: An Approach for Improving Computer Inspection","authors":"Luís Filipe da Cruz Nassif, Eduardo R. Hruschka","doi":"10.1109/ICMLA.2011.59","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.59","url":null,"abstract":"In computer forensic analysis, hundreds of thousands of files are usually examined. Much of those files consist of unstructured text, whose analysis by computer examiners is difficult to be performed. In this context, automated methods of analysis are of great interest. In particular, algorithms for clustering documents can facilitate the discovery of new and useful knowledge from the documents under analysis. We present an approach that applies clustering algorithms to forensic analysis of computers seized in police investigations. We illustrate the proposed approach by carrying out experimentation with five clustering algorithms (K-means, K-medoids, Single Link, Complete Link, and Average Link) applied to five datasets obtained from computers seized in real-world investigations. In addition, two relative validity indexes were used to automatically estimate the number of clusters. Related studies in the literature are significantly more limited than our study. Our experiments show that the Average Link and Complete Link algorithms provide the best results for our application domain. If suitably initialized, partitional algorithms (K-means and K-medoids) can also yield to very good results. Finally, we also present and discuss practical results that can be useful for researchers and practitioners of forensic computing.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129049457","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}

{"title":"Predictive Subspace Clustering","authors":"B. McWilliams, G. Montana","doi":"10.1109/ICMLA.2011.117","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.117","url":null,"abstract":"The problem of detecting clusters in high-dimensional data is increasingly common in machine learning applications, for instance in computer vision and bioinformatics. Recently, a number of approaches in the field of subspace clustering have been proposed which search for clusters in subspaces of unknown dimensions. Learning the number of clusters, the dimension of each subspace, and the correct assignments is a challenging task, and many existing algorithms often perform poorly in the presence of subspaces that have different dimensions and possibly overlap, or are otherwise computationally expensive. In this work we present a novel approach to subspace clustering that learns the numbers of clusters and the dimensionality of each subspace in an efficient way. We assume that the data points in each cluster are well represented in low-dimensions by a PCA model. We propose a measure of predictive influence of data points modelled by PCA which we minimise to drive the clustering process. The proposed predictive subspace clustering algorithm is assessed on both simulated data and on the popular Yale faces database where state-of-the-art performance and speed are obtained.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130791973","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}