H. S. Kuyuk, O. Ozkan, R. Kayikci, S. Bayraktaroglu
Energy plays a fundamental role in an economy. Turkey has the world's 15th largest GDP-Purchasing power parity and 17th largest Nominal GDP. Economists and political scientists classify Turkey as a newly industrialized country. In this study, an alternative model for Turkey’s energy consumption is proposed for the time between 1980 and 2004. Artificial neural network based model (ANN) is preferred as a forecasting tool. Gross domestic product (GDP), which is based on purchasing power parity, industrial production index and total population are utilized in the model. It is found that the energy consumption has direct relations with the Industrial Production Index. Moreover, population and GDP has causality effects.
{"title":"Modelling Turkey's Energy Consumption Based on Artificial Neural Network","authors":"H. S. Kuyuk, O. Ozkan, R. Kayikci, S. Bayraktaroglu","doi":"10.1109/ICMLA.2010.105","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.105","url":null,"abstract":"Energy plays a fundamental role in an economy. Turkey has the world's 15th largest GDP-Purchasing power parity and 17th largest Nominal GDP. Economists and political scientists classify Turkey as a newly industrialized country. In this study, an alternative model for Turkey’s energy consumption is proposed for the time between 1980 and 2004. Artificial neural network based model (ANN) is preferred as a forecasting tool. Gross domestic product (GDP), which is based on purchasing power parity, industrial production index and total population are utilized in the model. It is found that the energy consumption has direct relations with the Industrial Production Index. Moreover, population and GDP has causality effects.","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":"129265361","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}
S. You, E. Cansizoglu, Deniz Erdoğmuş, J. Tanyi, Jayashree Kalpathy-Cramer
Radiation therapy is one of the most effective options used in the treatment of about half of all people with cancer. A critical goal in radiation therapy is to deliver optimal radiation doses to the observed tumor while sparing the surrounding healthy tissues. Radiation oncologists typically manually delineate normal and diseased structures on three-dimensional computed tomography~(3D-CT) scans. Manual delineation is a labor intensive, tedious and time-consuming task. In recent years, concerns about respiration induced motion have led to the popularity of four-dimensional computed tomography~(4D-CT) for the tracking of tumors and deformation of organs. However, as manually contouring in all phases would be prohibitively expensive, the development of fast, robust, and automatic segmentation tools has been an active area of research in 4D radiotherapy. In this paper, we describe a novel application of principal surfaces for the propagation of contours in 4D-CT studies. Regions of interest~(ROIs) are manually delineated slice-by-slice in the reference 3D-CT scans. Edges are detected on all of the slices of the target 3D-CT phase. A kernel density estimation~(KDE) based on the detected edges is then calculated. The principal surface algorithm is applied to find the ridges of the edge KDE to provide the object contours. Manually drawn contours from the reference phase are used as an initialization. Contours of ROIs are propagated recursively in all consecutive phases to complete a respiration cycle. Results are provided for a phantom data set of simulated tumor motion as well as on a de-identified data set of the lung of a patient. Evaluation of the efficacy of automatic segmentation in organs and tumors are based on the comparison between manually drawn contours and automatically delineated contours. The Dice coefficients are approximately 0.97 for the lung tumor on the phantom data sets and 0.95 for the patient data sets. The centroid distances between manually delineated lung volume and automatically segmented lung volume in each CT direction are
{"title":"A Novel Application of Principal Surfaces to Segmentation in 4D-CT for Radiation Treatment Planning","authors":"S. You, E. Cansizoglu, Deniz Erdoğmuş, J. Tanyi, Jayashree Kalpathy-Cramer","doi":"10.1109/ICMLA.2010.116","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.116","url":null,"abstract":"Radiation therapy is one of the most effective options used in the treatment of about half of all people with cancer. A critical goal in radiation therapy is to deliver optimal radiation doses to the observed tumor while sparing the surrounding healthy tissues. Radiation oncologists typically manually delineate normal and diseased structures on three-dimensional computed tomography~(3D-CT) scans. Manual delineation is a labor intensive, tedious and time-consuming task. In recent years, concerns about respiration induced motion have led to the popularity of four-dimensional computed tomography~(4D-CT) for the tracking of tumors and deformation of organs. However, as manually contouring in all phases would be prohibitively expensive, the development of fast, robust, and automatic segmentation tools has been an active area of research in 4D radiotherapy. In this paper, we describe a novel application of principal surfaces for the propagation of contours in 4D-CT studies. Regions of interest~(ROIs) are manually delineated slice-by-slice in the reference 3D-CT scans. Edges are detected on all of the slices of the target 3D-CT phase. A kernel density estimation~(KDE) based on the detected edges is then calculated. The principal surface algorithm is applied to find the ridges of the edge KDE to provide the object contours. Manually drawn contours from the reference phase are used as an initialization. Contours of ROIs are propagated recursively in all consecutive phases to complete a respiration cycle. Results are provided for a phantom data set of simulated tumor motion as well as on a de-identified data set of the lung of a patient. Evaluation of the efficacy of automatic segmentation in organs and tumors are based on the comparison between manually drawn contours and automatically delineated contours. The Dice coefficients are approximately 0.97 for the lung tumor on the phantom data sets and 0.95 for the patient data sets. The centroid distances between manually delineated lung volume and automatically segmented lung volume in each CT direction are","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":"125472870","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}
F. Rezwan, Yi Sun, N. Davey, R. Adams, A. Rust, M. Robinson
Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may or may not contain binding sites. This poses problems with negative data in a trainable classifier. Here we show that using randomized negative data gives a large boost in classifier performance when compared to the original labeled data.
{"title":"Using Randomised Vectors in Transcription Factor Binding Site Predictions","authors":"F. Rezwan, Yi Sun, N. Davey, R. Adams, A. Rust, M. Robinson","doi":"10.1109/ICMLA.2010.82","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.82","url":null,"abstract":"Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may or may not contain binding sites. This poses problems with negative data in a trainable classifier. Here we show that using randomized negative data gives a large boost in classifier performance when compared to the original labeled data.","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":"126272496","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}
Wind power may present undesirable discontinuities and fluctuations due to considerable variations in wind speed, which may affect adversely the smooth operation of the grid. Effective wind forecast is essential in order to report the amount of energy supply with high accuracy, which is crucial for planning energy resources for power system operators. Variations in wind power cannot be sufficiently estimated by persistence type basic forecasting methods particularly in medium and long terms. Therefore a new statistical method is presented here in this paper based on independent component analysis (ICA) and autoregressive (AR) model. ICA is utilized in order to exploit the hidden factors which may exist in the wind speed time-series. It is understood that ICA, especially ICA methods based on exploiting the time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.
{"title":"Wind Speed Forecasting Based on Second Order Blind Identification and Autoregressive Model","authors":"U. Fırat, Ş. Engin, M. Saraçlar, Aysin Ertüzün","doi":"10.1109/ICMLA.2010.106","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.106","url":null,"abstract":"Wind power may present undesirable discontinuities and fluctuations due to considerable variations in wind speed, which may affect adversely the smooth operation of the grid. Effective wind forecast is essential in order to report the amount of energy supply with high accuracy, which is crucial for planning energy resources for power system operators. Variations in wind power cannot be sufficiently estimated by persistence type basic forecasting methods particularly in medium and long terms. Therefore a new statistical method is presented here in this paper based on independent component analysis (ICA) and autoregressive (AR) model. ICA is utilized in order to exploit the hidden factors which may exist in the wind speed time-series. It is understood that ICA, especially ICA methods based on exploiting the time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.","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":"122193838","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}
D. Dittman, T. Khoshgoftaar, Randall Wald, J. V. Hulse
One of today’s most important scientific research topics is discovering the genetic links between cancers. This paper contains the results of a comparison of three different cancers (breast, colon, and lung) based on the results of feature selection techniques on a data set created from DNA micro array data consisting of samples from all three cancers. The data was run through a set of eighteen feature rankers which ordered the genes by importance with respect to a targeted cancer. This process was repeated three times, each time with a different target cancer. The rankings were then compared, keeping each feature ranker static while varying the cancers being compared. The cancers were evaluated both in pairs and all together, for matching genes. The results of the comparison show a large correlation between the two known hereditary cancers, breast and colon, and little correlation between lung cancer and the other cancers. This is the first study to apply eighteen different feature rankers in a bioinformatics case study, eleven of which were recently proposed and implemented by our research team.
{"title":"Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques","authors":"D. Dittman, T. Khoshgoftaar, Randall Wald, J. V. Hulse","doi":"10.1109/ICMLA.2010.29","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.29","url":null,"abstract":"One of today’s most important scientific research topics is discovering the genetic links between cancers. This paper contains the results of a comparison of three different cancers (breast, colon, and lung) based on the results of feature selection techniques on a data set created from DNA micro array data consisting of samples from all three cancers. The data was run through a set of eighteen feature rankers which ordered the genes by importance with respect to a targeted cancer. This process was repeated three times, each time with a different target cancer. The rankings were then compared, keeping each feature ranker static while varying the cancers being compared. The cancers were evaluated both in pairs and all together, for matching genes. The results of the comparison show a large correlation between the two known hereditary cancers, breast and colon, and little correlation between lung cancer and the other cancers. This is the first study to apply eighteen different feature rankers in a bioinformatics case study, eleven of which were recently proposed and implemented by our research team.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"52 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114133314","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}
How can we get such reliable behavior from the mind when the brain is made up of such unreliable elements as neurons? We propose that the answer is related to the emergence of stable brain states and we offer a model that illustrates how such states could arise. We discuss a new ab initio nonlinear dynamical multi-scale model that will serve as the foundation for an associative memory. Scale 0 consists of spiking Hodgkin-Huxley (HH) neurons. Scale 1 consists of components that are made up of large populations of HH neurons whose topological structure evolves according to a Hebbian-plasticity rule based on synchronous firing. The component's state is captured by the variance of phase synchrony for the population. Many such components are sparsely connected to form a large network, whose state can be captured by the n-tuple consisting of the individual states of each member component. Scale 2 takes the state of the overall network and upon examining the particular interrelationships of each component (determining how the state of one component affects the state of others) is able to generate a class of trajectories that is multistationary and stable periodic. Such a class we consider a memory, the encoding of many such memories leads to the creation of a robust associative memory. The details of the different scales are examined.
{"title":"Nonlinear Dynamical Multi-Scale Model of Associative Memory","authors":"A. Duda, S. Levinson","doi":"10.1109/ICMLA.2010.135","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.135","url":null,"abstract":"How can we get such reliable behavior from the mind when the brain is made up of such unreliable elements as neurons? We propose that the answer is related to the emergence of stable brain states and we offer a model that illustrates how such states could arise. We discuss a new ab initio nonlinear dynamical multi-scale model that will serve as the foundation for an associative memory. Scale 0 consists of spiking Hodgkin-Huxley (HH) neurons. Scale 1 consists of components that are made up of large populations of HH neurons whose topological structure evolves according to a Hebbian-plasticity rule based on synchronous firing. The component's state is captured by the variance of phase synchrony for the population. Many such components are sparsely connected to form a large network, whose state can be captured by the n-tuple consisting of the individual states of each member component. Scale 2 takes the state of the overall network and upon examining the particular interrelationships of each component (determining how the state of one component affects the state of others) is able to generate a class of trajectories that is multistationary and stable periodic. Such a class we consider a memory, the encoding of many such memories leads to the creation of a robust associative memory. The details of the different scales are examined.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"2 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":"115317103","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}
A Minimum Classification Error (MCE) based recognition system that also estimates a global feature transformation matrix has been implemented. Unlike earlier studies, we make the explicit assumption that the covariance matrix of the Gaussian mixtures is diagonal when estimating the transformation matrix. This is necessary for mathematical consistency between the model and the transformation matrix estimates. Experimental results show a reduction of up to 50% in the word error rate as compared to Maximum Likelihood estimation.
{"title":"Feature Transformation and Model Design Using Minimum Classification Error","authors":"M. Ratnagiri, L. Rabiner, B. Juang","doi":"10.1109/ICMLA.2010.122","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.122","url":null,"abstract":"A Minimum Classification Error (MCE) based recognition system that also estimates a global feature transformation matrix has been implemented. Unlike earlier studies, we make the explicit assumption that the covariance matrix of the Gaussian mixtures is diagonal when estimating the transformation matrix. This is necessary for mathematical consistency between the model and the transformation matrix estimates. Experimental results show a reduction of up to 50% in the word error rate as compared to Maximum Likelihood estimation.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"46 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":"116722226","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}
Malak Alshawabkeh, Micha Moffie, Fatemeh Azmandian, J. Aslam, Jennifer G. Dy, D. Kaeli
Virtualization is becoming an increasingly popular service hosting platform. Recently, intrusion detection systems (IDSs) which utilize virtualization have been introduced. One particular challenge present in current virtualization-based IDS systems is considered in this paper. IDS systems are commonly faced with high-dimensionality imbalanced data. Improved feature selection methods are needed to achieve more accurate detection when presented with imbalanced data. These methods must select the right set of features which will lead to a lower number of false alarms and higher correct detection rates. In this paper we propose a new Boosting-based feature selection that evaluates the relative importance of individual features using the fractional absolute confidence that Boosting produces. Our approach accounts for the sample distributions by optimizing for the area under the Receive Operating Characteristic (ROC) curve (i.e., Area Under the Curve(AUC)). Empirical results on different commercial virtual appliances and malwares indicate that proper input feature selection is key if we want an effective virtualization-based IDS that is lightweight, efficient and effective.
{"title":"Effective Virtual Machine Monitor Intrusion Detection Using Feature Selection on Highly Imbalanced Data","authors":"Malak Alshawabkeh, Micha Moffie, Fatemeh Azmandian, J. Aslam, Jennifer G. Dy, D. Kaeli","doi":"10.1109/ICMLA.2010.127","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.127","url":null,"abstract":"Virtualization is becoming an increasingly popular service hosting platform. Recently, intrusion detection systems (IDSs) which utilize virtualization have been introduced. One particular challenge present in current virtualization-based IDS systems is considered in this paper. IDS systems are commonly faced with high-dimensionality imbalanced data. Improved feature selection methods are needed to achieve more accurate detection when presented with imbalanced data. These methods must select the right set of features which will lead to a lower number of false alarms and higher correct detection rates. In this paper we propose a new Boosting-based feature selection that evaluates the relative importance of individual features using the fractional absolute confidence that Boosting produces. Our approach accounts for the sample distributions by optimizing for the area under the Receive Operating Characteristic (ROC) curve (i.e., Area Under the Curve(AUC)). Empirical results on different commercial virtual appliances and malwares indicate that proper input feature selection is key if we want an effective virtualization-based IDS that is lightweight, efficient and effective.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"31 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":"125636207","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}
Gustavo E. A. P. A. Batista, Bilson J. L. Campana, Eamonn J. Keogh
Each year, insect-borne diseases kill more than one million people, and harmful insects destroy tens of billions of dollars worth of crops and livestock. At the same time, beneficial insects pollinate three-quarters of all food consumed by humans. Given the extraordinary impact of insects on human life, it is somewhat surprising that machine learning has made very little impact on understanding (and hence, controlling) insects. In this work we discuss why this is the case, and argue that a confluence of facts make the time ripe for machine learning research to reach out to the entomological community and help them solve some important problems. As a concrete example, we show how we can solve an important classification problem in commercial entomology by leveraging off recent progress in shape, color and texture measures.
{"title":"Classification of Live Moths Combining Texture, Color and Shape Primitives","authors":"Gustavo E. A. P. A. Batista, Bilson J. L. Campana, Eamonn J. Keogh","doi":"10.1109/ICMLA.2010.142","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.142","url":null,"abstract":"Each year, insect-borne diseases kill more than one million people, and harmful insects destroy tens of billions of dollars worth of crops and livestock. At the same time, beneficial insects pollinate three-quarters of all food consumed by humans. Given the extraordinary impact of insects on human life, it is somewhat surprising that machine learning has made very little impact on understanding (and hence, controlling) insects. In this work we discuss why this is the case, and argue that a confluence of facts make the time ripe for machine learning research to reach out to the entomological community and help them solve some important problems. As a concrete example, we show how we can solve an important classification problem in commercial entomology by leveraging off recent progress in shape, color and texture measures.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"42 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":"125075596","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}
We present an approach for grouping single-speaker speech segments into speaker-specific clusters. Our approach is based on applying the K-means clustering algorithm to a suitable discriminant subspace, where the euclidean distance reflect speaker differences. A core feature of our approach is approximating speaker-conditional statistics, that are not available, with single-speaker segments statistics, which can be evaluated, thus making possible to apply the LDA algorithm for finding the optimal discriminative subspace, using unlabeled data. To illustrate our method, we present examples of clusters generated by our approach when applied to the ICMLA 2010 Speaker Clustering Challenge datasets.
{"title":"Unsupervised Speaker Clustering in a Linear Discriminant Subspace","authors":"Theodoros Giannakopoulos, Sergios Petridis","doi":"10.1109/ICMLA.2010.159","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.159","url":null,"abstract":"We present an approach for grouping single-speaker speech segments into speaker-specific clusters. Our approach is based on applying the K-means clustering algorithm to a suitable discriminant subspace, where the euclidean distance reflect speaker differences. A core feature of our approach is approximating speaker-conditional statistics, that are not available, with single-speaker segments statistics, which can be evaluated, thus making possible to apply the LDA algorithm for finding the optimal discriminative subspace, using unlabeled data. To illustrate our method, we present examples of clusters generated by our approach when applied to the ICMLA 2010 Speaker Clustering Challenge datasets.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"10 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":"128085352","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}
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