In this paper, we present a heuristic-based algorithm to learn discriminative MLN structures automatically, directly from a training dataset. The algorithm heuristically transforms the relational dataset into boolean tables from which it builds candidate clauses for learning the final MLN. Comparisons to the state-of-the-art structure learning algorithms for MLNs in the three real-world domains show that the proposed algorithm outperforms them in terms of the conditional log likelihood (CLL), and the area under the precision-recall curve (AUC).
{"title":"Heuristic Method for Discriminative Structure Learning of Markov Logic Networks","authors":"Quang-Thang Dinh, M. Exbrayat, Christel Vrain","doi":"10.1109/ICMLA.2010.31","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.31","url":null,"abstract":"In this paper, we present a heuristic-based algorithm to learn discriminative MLN structures automatically, directly from a training dataset. The algorithm heuristically transforms the relational dataset into boolean tables from which it builds candidate clauses for learning the final MLN. Comparisons to the state-of-the-art structure learning algorithms for MLNs in the three real-world domains show that the proposed algorithm outperforms them in terms of the conditional log likelihood (CLL), and the area under the precision-recall curve (AUC).","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"16 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":"123863260","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}
Tianming Hu, Chuanren Liu, Jing Sun, S. Sung, P. Ng
Conventional k-means only considers pair wise similarity during cluster assignment, which aims to minimizing the distance of points to their nearest cluster centroids. In high dimensional space like document datasets, however, two points may be nearest neighbors without belonging to the same class. Thus pair wise similarity alone is often insufficient for class prediction in such space. To that end, in this paper, we propose to augment k-means with pair wise constraints generated from group similarity-based hyper clique patterns, which consist of strongly affiliated objects and serve as more reliable seeds for classification. Experiments with real-world datasets show that, with such constraints from quality hyper clique patterns, we can improve the clustering results in terms of various external criteria. Also, our experiments indicate that even if few constraints are violated in the original result of k-means, imposing many quality constraints may still bring gain of performance.
{"title":"Pairwise Constrained Clustering with Group Similarity-Based Patterns","authors":"Tianming Hu, Chuanren Liu, Jing Sun, S. Sung, P. Ng","doi":"10.1109/ICMLA.2010.45","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.45","url":null,"abstract":"Conventional k-means only considers pair wise similarity during cluster assignment, which aims to minimizing the distance of points to their nearest cluster centroids. In high dimensional space like document datasets, however, two points may be nearest neighbors without belonging to the same class. Thus pair wise similarity alone is often insufficient for class prediction in such space. To that end, in this paper, we propose to augment k-means with pair wise constraints generated from group similarity-based hyper clique patterns, which consist of strongly affiliated objects and serve as more reliable seeds for classification. Experiments with real-world datasets show that, with such constraints from quality hyper clique patterns, we can improve the clustering results in terms of various external criteria. Also, our experiments indicate that even if few constraints are violated in the original result of k-means, imposing many quality constraints may still bring gain of performance.","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":"116704574","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}
Using microfossil-based transfer functions, domain scientists from the field of pale oceanography seek to reconstruct environmental conditions at various times in the past. This is accomplished by first determining a quantitative relationship between a forcing function, such as temperature, and the modern for aminiferal response using a calibration data set based on environmental data from an oceanographic atlas and faunas generally extracted from sediment core tops. The method can be employed with a variety of environmental variables, but reconstruction of surface temperature is often the objective. The relationship developed using this training or calibration data set is then applied to down core data to infer past environmental conditions. The statistical methods that have been previously applied in this area can be grouped into three categories: linear regression based approaches, locally weighted regressions and neural networks. In addition to introducing some other learning algorithms including regression trees, bagging trees, random forest and support vector regression to this domain, in this study we suggest the use of model combination approaches to enhance the precision of estimation. By initializing with a pool of diverse predictors using a variety of learning algorithms and different samplings from the training and attribute set, a genetic algorithm was applied to select the best team of predictors. The optimal team was dominated by artificial neural network predictors suggesting their superiority over other methods tested with this type of data. The results also show the efficacy of the proposed approach compared to the other models.
{"title":"Evolutionary Selection of Regressional Predictors to Enhance the Performance of Microfossil-Based Paleotemperture Proxies","authors":"A. Assareh, L. Volkert, J. Ortiz","doi":"10.1109/ICMLA.2010.63","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.63","url":null,"abstract":"Using microfossil-based transfer functions, domain scientists from the field of pale oceanography seek to reconstruct environmental conditions at various times in the past. This is accomplished by first determining a quantitative relationship between a forcing function, such as temperature, and the modern for aminiferal response using a calibration data set based on environmental data from an oceanographic atlas and faunas generally extracted from sediment core tops. The method can be employed with a variety of environmental variables, but reconstruction of surface temperature is often the objective. The relationship developed using this training or calibration data set is then applied to down core data to infer past environmental conditions. The statistical methods that have been previously applied in this area can be grouped into three categories: linear regression based approaches, locally weighted regressions and neural networks. In addition to introducing some other learning algorithms including regression trees, bagging trees, random forest and support vector regression to this domain, in this study we suggest the use of model combination approaches to enhance the precision of estimation. By initializing with a pool of diverse predictors using a variety of learning algorithms and different samplings from the training and attribute set, a genetic algorithm was applied to select the best team of predictors. The optimal team was dominated by artificial neural network predictors suggesting their superiority over other methods tested with this type of data. The results also show the efficacy of the proposed approach compared to the other models.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"39 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":"117092696","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}
Neuropathic pain (NeuP) is often more difficult to treat than other types of chronic pain. The ability to predict outcomes in NeuP, such as response to specific therapies and return to work, would have tremendous value to both patients and society. In this work, we propose an adaptive clustering algorithm using the Neuropathic Pain Scale (NPS) to develop a set of standard patient templates. These templates may be useful in studying treatment response in NeuP. The approach is evaluated on 108 subjects' baseline data and results demonstrate the efficacy of utilizing neuropathic pain scale (NPS) metrics and our proposed method.
{"title":"Neuropathic Pain Scale Based Clustering for Subgroup Analysis in Pain Medicine","authors":"Guangzhi Qu, Hui Wu, I. Sethi, C. Hartrick","doi":"10.1109/ICMLA.2010.51","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.51","url":null,"abstract":"Neuropathic pain (NeuP) is often more difficult to treat than other types of chronic pain. The ability to predict outcomes in NeuP, such as response to specific therapies and return to work, would have tremendous value to both patients and society. In this work, we propose an adaptive clustering algorithm using the Neuropathic Pain Scale (NPS) to develop a set of standard patient templates. These templates may be useful in studying treatment response in NeuP. The approach is evaluated on 108 subjects' baseline data and results demonstrate the efficacy of utilizing neuropathic pain scale (NPS) metrics and our proposed method.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"88 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":"115430121","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}
This paper presents a novel method for semantic understanding of sports matches by extracting and ranking events within a match by interestingness. Using audio feature extraction, a system is presented which is able to segment between studio and pitch side broadcast. Key events within Rugby Union matches are then identified based on crowd excitation levels and referee whistles. This identifies individual interesting events and a timeline of interestingness estimation allowing viewers to navigate to sections of the broadcast where interesting sections of play occur.
{"title":"Interestingness Detection in Sports Audio Broadcasts","authors":"Sam Davies, Denise Bland","doi":"10.1109/ICMLA.2010.99","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.99","url":null,"abstract":"This paper presents a novel method for semantic understanding of sports matches by extracting and ranking events within a match by interestingness. Using audio feature extraction, a system is presented which is able to segment between studio and pitch side broadcast. Key events within Rugby Union matches are then identified based on crowd excitation levels and referee whistles. This identifies individual interesting events and a timeline of interestingness estimation allowing viewers to navigate to sections of the broadcast where interesting sections of play occur.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"15 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":"126706789","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}
As modern processors can execute instructions at far greater rates than these instructions can be retrieved from main memory, computer systems commonly include caches that speed up access times. While these improve average execution times, they introduce additional complexity in determining the Worst Case Execution Times crucial for Real-Time Systems. In this paper, an approach is presented that utilises Bayesian Networks in order to more accurately estimate the worst-case caching behaviour of programs. With this method, a Bayesian Network is learned from traces of program execution that allows both constructive and destructive dependencies between instructions to be determined and a joint distribution over the number of cache hits to be found. Attention is given to the question of how the accuracy of the network depends on both the number of observations used for learning and the cardinality of the set of potential parents considered by the learning algorithm.
{"title":"Learning Bayesian Networks for Improved Instruction Cache Analysis","authors":"M. Bartlett, I. Bate, J. Cussens","doi":"10.1109/ICMLA.2010.68","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.68","url":null,"abstract":"As modern processors can execute instructions at far greater rates than these instructions can be retrieved from main memory, computer systems commonly include caches that speed up access times. While these improve average execution times, they introduce additional complexity in determining the Worst Case Execution Times crucial for Real-Time Systems. In this paper, an approach is presented that utilises Bayesian Networks in order to more accurately estimate the worst-case caching behaviour of programs. With this method, a Bayesian Network is learned from traces of program execution that allows both constructive and destructive dependencies between instructions to be determined and a joint distribution over the number of cache hits to be found. Attention is given to the question of how the accuracy of the network depends on both the number of observations used for learning and the cardinality of the set of potential parents considered by the learning algorithm.","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":"124350613","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}
Malik Tahir Hassan, Asim Karim, F. Javed, N. Arshad
In this paper, we propose and evaluate a self-optimization strategy for a clustering-based tag recommendation system. For tag recommendation, we use an efficient discriminative clustering approach. To develop our self-optimization strategy for this tag recommendation approach, we empirically investigate when and how to update the tag recommender with minimum human intervention. We present a nonlinear optimization model whose solution yields the clustering parameters that maximize the recommendation accuracy within an administrator specified time window. Evaluation on ``BibSonomy'' data produces promising results. For example, by using our self-optimization strategy a 6% increase in average F1 score is achieved when the administrator allows emph{up to} 2% drop in average F1 score in the last one thousand recommendations.
{"title":"Self-Optimizing a Clustering-based Tag Recommender for Social Bookmarking Systems","authors":"Malik Tahir Hassan, Asim Karim, F. Javed, N. Arshad","doi":"10.1109/ICMLA.2010.93","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.93","url":null,"abstract":"In this paper, we propose and evaluate a self-optimization strategy for a clustering-based tag recommendation system. For tag recommendation, we use an efficient discriminative clustering approach. To develop our self-optimization strategy for this tag recommendation approach, we empirically investigate when and how to update the tag recommender with minimum human intervention. We present a nonlinear optimization model whose solution yields the clustering parameters that maximize the recommendation accuracy within an administrator specified time window. Evaluation on ``BibSonomy'' data produces promising results. For example, by using our self-optimization strategy a 6% increase in average F1 score is achieved when the administrator allows emph{up to} 2% drop in average F1 score in the last one thousand recommendations.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"47 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":"125094822","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}
Imitation learning enables a learner to improve its abilities by observing others. Most robotic imitation learning systems only learn from demonstrators that are homogeneous physiologically (i.e. the same size and mode of locomotion) and in terms of skill level. To successfully learn from physically heterogeneous robots that may also vary in ability, the imitator must be able to abstract behaviours it observes and approximate them with its own actions, which may be very different than those of the demonstrator. This paper describes an approach to imitation learning from heterogeneous demonstrators, using global vision for observations. It supports learning from physiologically different demonstrators (wheeled and legged, of various sizes), and self-adapts to demonstrators with varying levels of skill. The latter allows a bias toward demonstrators that are successful in the domain, but also allows different parts of a task to be learned from different individuals (that is, worthwhile parts of a task can still be learned from a poorly-performing demonstrator). We assume the imitator has no initial knowledge of the observable effects of its own actions, and train a set of Hidden Markov Models to map observations to actions and create an understanding of the imitator's own abilities. We then use a combination of tracking sequences of primitives and predicting future primitives from existing combinations using forward models to learn abstract behaviours from the demonstrations of others. This approach is evaluated using a group of heterogeneous robots that have been previously used in RoboCup soccer competitions.
{"title":"Heterogeneous Imitation Learning from Demonstrators of Varying Physiology and Skill","authors":"Jeff Allen, J. Anderson","doi":"10.1109/ICMLA.2010.23","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.23","url":null,"abstract":"Imitation learning enables a learner to improve its abilities by observing others. Most robotic imitation learning systems only learn from demonstrators that are homogeneous physiologically (i.e. the same size and mode of locomotion) and in terms of skill level. To successfully learn from physically heterogeneous robots that may also vary in ability, the imitator must be able to abstract behaviours it observes and approximate them with its own actions, which may be very different than those of the demonstrator. This paper describes an approach to imitation learning from heterogeneous demonstrators, using global vision for observations. It supports learning from physiologically different demonstrators (wheeled and legged, of various sizes), and self-adapts to demonstrators with varying levels of skill. The latter allows a bias toward demonstrators that are successful in the domain, but also allows different parts of a task to be learned from different individuals (that is, worthwhile parts of a task can still be learned from a poorly-performing demonstrator). We assume the imitator has no initial knowledge of the observable effects of its own actions, and train a set of Hidden Markov Models to map observations to actions and create an understanding of the imitator's own abilities. We then use a combination of tracking sequences of primitives and predicting future primitives from existing combinations using forward models to learn abstract behaviours from the demonstrations of others. This approach is evaluated using a group of heterogeneous robots that have been previously used in RoboCup soccer competitions.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"18 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":"128376935","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}
The characterization of inter-segmental coordination patterns in hemi paretic gait is interesting to improve the management of hemiparetic patients. Indeed, the analysis of the coordination patterns can help clinician to establish patient diagnosis and to choose a treatment. The coordination patterns used in this article were obtained from the Continuous Relative Phase (CRP) measure in the sagittal plane. The CRP correlates angle positions and velocity of two segments, i.e. parts of the patient leg, over each phase of the gait cycle. Thigh-shank and shank-foot CRPs were measured for 66 hemiparetic patients, 27 healthy subjects and 14 patients pre and post treatment. CRPs signals are classified using a multi-classifier. This classification permits to discriminate gait patterns for hemiparetic and healthy subjects. The multi-classifier is based on a structural and a statistical approaches used in parallel. The structural part of the proposed hybrid method keeps links between the data issued from CRPs and the statistical part converts CRPs into spatial scalar parameters. Then, using a similarity measure this approach permits to quantify the global gait coordination improvement of patients after a therapeutic treatment. The proposed approach uses only interpretable parameters in order to let the classification results be physically understandable.
{"title":"A Hybrid Multi-classifier to Characterize and Interpret Hemiparetic Patients Gait Coordination","authors":"Laurent Hartert, M. S. Mouchaweh","doi":"10.1109/ICMLA.2010.90","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.90","url":null,"abstract":"The characterization of inter-segmental coordination patterns in hemi paretic gait is interesting to improve the management of hemiparetic patients. Indeed, the analysis of the coordination patterns can help clinician to establish patient diagnosis and to choose a treatment. The coordination patterns used in this article were obtained from the Continuous Relative Phase (CRP) measure in the sagittal plane. The CRP correlates angle positions and velocity of two segments, i.e. parts of the patient leg, over each phase of the gait cycle. Thigh-shank and shank-foot CRPs were measured for 66 hemiparetic patients, 27 healthy subjects and 14 patients pre and post treatment. CRPs signals are classified using a multi-classifier. This classification permits to discriminate gait patterns for hemiparetic and healthy subjects. The multi-classifier is based on a structural and a statistical approaches used in parallel. The structural part of the proposed hybrid method keeps links between the data issued from CRPs and the statistical part converts CRPs into spatial scalar parameters. Then, using a similarity measure this approach permits to quantify the global gait coordination improvement of patients after a therapeutic treatment. The proposed approach uses only interpretable parameters in order to let the classification results be physically understandable.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"62 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":"123122894","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}
Kilian Förster, Samuel Monteleone, Alberto Calatroni, D. Roggen, G. Tröster
Non-stationary data distributions are a challenge in activity recognition from body worn motion sensors. Classifier models have to be adapted online to maintain a high recognition performance. Typical approaches for online learning are either unsupervised and potentially unstable, or require ground truth information which may be expensive to obtain. As an alternative we propose a teacher signal that can be provided by the user in a minimally obtrusive way. It indicates if the predicted activity for a feature vector is correct or wrong. To exploit this information we propose a novel incremental online learning strategy to adapt a k-nearest-neighbor classifier from instances that are indicated to be correctly or wrongly classified. We characterize our approach on an artificial dataset with abrupt distribution change that simulates a new user of an activity recognition system. The adapted classifier reaches the same accuracy as a classifier trained specifically for the new data distribution. The learning based on the provided correct - error signal also results in a faster learning speed compared to online learning from ground truth. We validate our approach on a real world gesture recognition dataset. The adapted classifiers achieve an accuracy of 78.6% compared to the subject independent baseline of 68.3%.
{"title":"Incremental kNN Classifier Exploiting Correct-Error Teacher for Activity Recognition","authors":"Kilian Förster, Samuel Monteleone, Alberto Calatroni, D. Roggen, G. Tröster","doi":"10.1109/ICMLA.2010.72","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.72","url":null,"abstract":"Non-stationary data distributions are a challenge in activity recognition from body worn motion sensors. Classifier models have to be adapted online to maintain a high recognition performance. Typical approaches for online learning are either unsupervised and potentially unstable, or require ground truth information which may be expensive to obtain. As an alternative we propose a teacher signal that can be provided by the user in a minimally obtrusive way. It indicates if the predicted activity for a feature vector is correct or wrong. To exploit this information we propose a novel incremental online learning strategy to adapt a k-nearest-neighbor classifier from instances that are indicated to be correctly or wrongly classified. We characterize our approach on an artificial dataset with abrupt distribution change that simulates a new user of an activity recognition system. The adapted classifier reaches the same accuracy as a classifier trained specifically for the new data distribution. The learning based on the provided correct - error signal also results in a faster learning speed compared to online learning from ground truth. We validate our approach on a real world gesture recognition dataset. The adapted classifiers achieve an accuracy of 78.6% compared to the subject independent baseline of 68.3%.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"64 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":"133980943","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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