2010 Ninth International Conference on Machine Learning and Applications最新文献

{"title":"Speeding Up Greedy Forward Selection for Regularized Least-Squares","authors":"T. Pahikkala, A. Airola, T. Salakoski","doi":"10.1109/ICMLA.2010.55","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.55","url":null,"abstract":"We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration adds the feature whose addition provides the best leave-one-out cross-validation performance. Our method is considerably faster than the previously proposed ones, since its time complexity is linear in the number of training examples, the number of features in the original data set, and the desired size of the set of selected features. Therefore, as a side effect we obtain a new training algorithm for learning sparse linear RLS predictors which can be used for large scale learning. This speed is possible due to matrix calculus based short-cuts for leave-one-out and feature addition. We experimentally demonstrate the scalability of our algorithm compared to previously proposed implementations.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"6 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":"132189739","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":"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}

{"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}

{"title":"Using a Bayesian Feature-selection Algorithm to Identify Dose-response Models Based on the Shape of the 3D Dose-distribution: An Example from a Head-and-neck Cancer Trial","authors":"F. Buettner, S. Gulliford, S. Webb, M. Partridge, A. Miah, K. Harrington, C. Nutting","doi":"10.1109/ICMLA.2010.113","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.113","url":null,"abstract":"A reduction in salivary flow and xerostomia are common side-effects after radiotherapy of head and neck tumours. Xerostomia can be modeled based on the dose to the parotid glands. To date, all spatial information has been discarded and dose-response models are usually reduced to the mean dose. We present novel morphological dose-response models and use multivariate Bayesian logistic regression to model xerostomia. We use 3D invariant statistical moments as morphometric descriptors to quantify the shape of the 3D dose distribution. As this results in a very high number of potential predictors, we apply a Bayesian variable-selection algorithm to find the best model based on any subset of all potential predictors. To do this, we determine the posterior probabilities of being the best model for all potential models and calculate the marginal probabilities that a variable should be included in a model. This was done using a Reversible Jump Markov Chain Monte Carlo algorithm. The performance of the best model was quantified using the deviance information criterion and a leave-one-out cross-validation (LOOCV). This methodology was applied to 64 head and neck cancer patients treated with either intensity-modulated radiotherapy (IMRT) or conventional radiotherapy. Results show a substantial increase in both model-fit and area under the curve (AUC) when including morphological information compared to conventional mean-dose models. The best mean-dose model for IMRT patients only resulted in an AUC of 0.63 after LOOCV while the best morphological model had an AUC of 0.90. For conventional patients the mean-dose model and the morphological model had AUC of 0.55 and 0.86 respectively. For a joint model with all patients pooled together, the mean dose model had an AUC of 0.75 and the morphological model an AUC of 0.88. We have shown that invariant statistical moments are a good morphometric descriptor and by using Bayesian variable selection we were able to identify models with a substantially higher predictive power than conventional mean-dose models.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"30 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":"115880211","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":"Improved Unsupervised Clustering over Watershed-Based Clustering","authors":"Sai Venu Gopal Lolla, L. L. Hoberock","doi":"10.1109/ICMLA.2010.44","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.44","url":null,"abstract":"This paper improves upon an existing Watershed algorithm-based clustering method. The existing method uses an experimentally determined parameter to construct a density function. A better method for evaluating the cell/window size (used in the construction of the density function) is proposed, eliminating the need for arbitrary parameters. The algorithm has been tested on both published and unpublished synthetic data, and the results demonstrate that the proposed approach is able to accurately estimate the number of clusters present in the data.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"80 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":"121330345","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":"Bayesian Inferences and Forecasting in Spatial Time Series Models","authors":"Sung Duck Lee, Duck-Ki Kim","doi":"10.1109/ICMLA.2010.170","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.170","url":null,"abstract":"The spatial time series data can be viewed as a set of time series collected simultaneously at a number of spatial locations with time. For example, The Mumps data have a feature to infect adjacent broader regions in accordance with spatial location and time. Therefore, The spatial time series models have many parameters of space and time. In this paper, We propose the method of bayesian inferences and prediction in spatial time series models with a Gibbs Sampler in order to overcome convergence problem in numerical methods. Our results are illustrated by using the data set of mumps cases reported from the Korea Center for Disease Control and Prevention monthly over the years 2001-2009, as well as a simulation study.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"522 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":"114623439","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":"Dynamic Decision Method Based on Contextual Selection of Representation Subspaces","authors":"P. Beauseroy, A. Smolarz, Yuan Dong, Xiyan He","doi":"10.1109/ICMLA.2010.88","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.88","url":null,"abstract":"This paper presents a dynamical decision method derived from ensemble decision method. It is designed to be robust with respect to abrupt change of sensor response. Abrupt change may be caused by impulsive noise, sensor degradation or transmission fault in the case of an autonomous sensor network. It can also be caused by inconsistency of sensor responses due to local or sudden break of one monitored system property. The main idea is to divide the decision into several partial decisions and then to aggregate these to get the final one. The adaptation is the result of the aggregation process which aims at selecting and summarizing the partial decisions which are based on coherent information according to learnt models. The suggested method is presented. Experiments on a two-class image segmentation problem are performed and analyzed. The results assessed that the suggested method is more robust when an abrupt change occurs and is able to select efficiently the partial decision makers. This approach opens a wide field of applications and results are very encouraging.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"141 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":"124463279","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":"Multi-Agent Inverse Reinforcement Learning","authors":"Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, K. Kersting, J. Shavlik","doi":"10.1109/ICMLA.2010.65","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.65","url":null,"abstract":"Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship learning. We introduce the problem of multi-agent inverse reinforcement learning, where reward functions of multiple agents are learned by observing their uncoordinated behavior. A centralized controller then learns to coordinate their behavior by optimizing a weighted sum of reward functions of all the agents. We evaluate our approach on a traffic-routing domain, in which a controller coordinates actions of multiple traffic signals to regulate traffic density. We show that the learner is not only able to match but even significantly outperform the expert.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"13 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":"125044165","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":"A Probabilistic Graphical Model of Quantum Systems","authors":"Chen-Hsiang Yeang","doi":"10.1109/ICMLA.2010.30","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.30","url":null,"abstract":"Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomplete and scattered. To integrate the distributed information we propose a quantum version of probabilistic graphical models. Variables in the model (quantum states and measurement outcomes) are linked by several types of operators (unitary, measurement, and merge/split operators). We propose algorithms for three machine learning tasks in quantum probabilistic graphical models: a belief propagation algorithm for inference of unknown states, an iterative algorithm for simultaneous estimation of parameter values and hidden states, and an active learning algorithm to select measurement operators based on observed evidence. We validate these algorithms on simulated data and point out future extensions toward a more comprehensive theory of quantum probabilistic graphical models.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"34 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":"124517947","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":"Hybridization of Base Classifiers of Random Subsample Ensembles for Enhanced Performance in High Dimensional Feature Spaces","authors":"Santhosh Pathical, G. Serpen","doi":"10.1109/ICMLA.2010.118","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.118","url":null,"abstract":"This paper presents a simulation-based empirical study of the performance profile of random sub sample ensembles with a hybrid mix of base learner composition in high dimensional feature spaces. The performance of hybrid random sub sample ensemble that uses a combination of C4.5, k-nearest neighbor (kNN) and naïve Bayes base learners is assessed through statistical testing in comparison to those of homogeneous random sub sample ensembles that employ only one type of base learner. Simulation study employs five datasets with up to 20K features from the UCI Machine Learning Repository. Random sub sampling without replacement is used to map the original high dimensional feature space of the five datasets to a multiplicity of lower dimensional feature subspaces. The simulation study explores the effect of certain design parameters that include the count of base classifiers and sub sampling rate on the performance of the hybrid random subspace ensemble. The ensemble architecture utilizes the voting combiner in all cases. Simulation results indicate that hybridization of base learners for random sub sample ensemble improves the prediction accuracy rates and projects a more robust performance.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"79 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":"124591146","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}