Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management最新文献
Community Discovery in networks is the problem of detecting, for each node, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive. We define the community discovery problem in multidimensional networks, where more than one connection may reside between any two nodes. We also introduce two measures able to characterize the communities found. Our experiments on real world multidimensional networks support the methodology proposed in this paper, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.
{"title":"Finding redundant and complementary communities in multidimensional networks","authors":"M. Berlingerio, M. Coscia, F. Giannotti","doi":"10.1145/2063576.2063921","DOIUrl":"https://doi.org/10.1145/2063576.2063921","url":null,"abstract":"Community Discovery in networks is the problem of detecting, for each node, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive. We define the community discovery problem in multidimensional networks, where more than one connection may reside between any two nodes. We also introduce two measures able to characterize the communities found. Our experiments on real world multidimensional networks support the methodology proposed in this paper, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"6 1","pages":"2181-2184"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81173088","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}
Chen Chen, Guoren Wang, Huilin Liu, Junchang Xin, Ye Yuan
A significant number of applications on graph require the key relations among a group of query nodes. Given a relational graph such as social network or biochemical interaction, an informative subgraph is urgent, which can best explain the relationships among a group of given query nodes. Based on Particle Swarm Optimization (PSO), a new framework of SISP (Searching the Informative Subgraph based on PSO) is proposed. SISP contains three key stages. In the initialization stage, a random spreading method is proposed, which can effectively guarantee the connectivity of the nodes in each particle; In the calculating stage of fitness, a fitness function is designed by incorporating a sign function with the goodness score; In the update stage, the intersection-based particle extension method and rule-based particle compression method are proposed. To evaluate the qualities of returned subgraphs, the appropriate calculating of goodness score is studied. Considering the importance and relevance of a node together, we present the PNR method, which makes the definition of informativeness more reliable and the returned subgraph more satisfying. At last, we present experiments on a real dataset and a synthetic dataset separately. The experimental results confirm that the proposed methods achieve increased accuracy and are efficient for any query set.
{"title":"SISP: a new framework for searching the informative subgraph based on PSO","authors":"Chen Chen, Guoren Wang, Huilin Liu, Junchang Xin, Ye Yuan","doi":"10.1145/2063576.2063645","DOIUrl":"https://doi.org/10.1145/2063576.2063645","url":null,"abstract":"A significant number of applications on graph require the key relations among a group of query nodes. Given a relational graph such as social network or biochemical interaction, an informative subgraph is urgent, which can best explain the relationships among a group of given query nodes. Based on Particle Swarm Optimization (PSO), a new framework of SISP (Searching the Informative Subgraph based on PSO) is proposed. SISP contains three key stages. In the initialization stage, a random spreading method is proposed, which can effectively guarantee the connectivity of the nodes in each particle; In the calculating stage of fitness, a fitness function is designed by incorporating a sign function with the goodness score; In the update stage, the intersection-based particle extension method and rule-based particle compression method are proposed. To evaluate the qualities of returned subgraphs, the appropriate calculating of goodness score is studied. Considering the importance and relevance of a node together, we present the PNR method, which makes the definition of informativeness more reliable and the returned subgraph more satisfying. At last, we present experiments on a real dataset and a synthetic dataset separately. The experimental results confirm that the proposed methods achieve increased accuracy and are efficient for any query set.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"57 1","pages":"453-462"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84809007","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}
Mrinmaya Sachan, Danish Contractor, T. Faruquie, L. V. Subramaniam
Social graphs have received renewed interest as a research topic with the advent of social networking websites. These online networks provide a rich source of data to study user relationships and interaction patterns on a large scale. In this paper, we propose a generative Bayesian model for extracting latent communities from a social graph. We assume that community memberships depend on topics of interest between users and the link relationships between them in the social graph topology. In addition, we make use of the nature of interaction to gauge user interests. Our model allows communities to be related to multiple topics and each user in the graph can be a member of multiple communities. This gives an insight into user interests and topical distribution in communities. We show the effectiveness of our model using a real world data set and also compare our model with existing community discovery methods.
{"title":"Probabilistic model for discovering topic based communities in social networks","authors":"Mrinmaya Sachan, Danish Contractor, T. Faruquie, L. V. Subramaniam","doi":"10.1145/2063576.2063963","DOIUrl":"https://doi.org/10.1145/2063576.2063963","url":null,"abstract":"Social graphs have received renewed interest as a research topic with the advent of social networking websites. These online networks provide a rich source of data to study user relationships and interaction patterns on a large scale. In this paper, we propose a generative Bayesian model for extracting latent communities from a social graph. We assume that community memberships depend on topics of interest between users and the link relationships between them in the social graph topology. In addition, we make use of the nature of interaction to gauge user interests. Our model allows communities to be related to multiple topics and each user in the graph can be a member of multiple communities. This gives an insight into user interests and topical distribution in communities. We show the effectiveness of our model using a real world data set and also compare our model with existing community discovery methods.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"17 1","pages":"2349-2352"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85296433","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}
Complex information extraction (IE) pipelines are becoming an integral component of most text processing frameworks. We introduce a first system to help IE users analyze extraction pipeline semantics and operator transformations interactively while debugging. This allows the effort to be proportional to the need, and to focus on the portions of the pipeline under the greatest suspicion. We present a generic debugger for running post-execution analysis of any IE pipeline consisting of arbitrary types of operators. For this, we propose an effective provenance model for IE pipelines which captures a variety of operator types, ranging from those for which full to no specifications are available. We have evaluated our proposed algorithms and provenance model on large-scale real-world extraction pipelines.
{"title":"Building a generic debugger for information extraction pipelines","authors":"A. Sarma, Alpa Jain, P. Bohannon","doi":"10.1145/2063576.2063933","DOIUrl":"https://doi.org/10.1145/2063576.2063933","url":null,"abstract":"Complex information extraction (IE) pipelines are becoming an integral component of most text processing frameworks. We introduce a first system to help IE users analyze extraction pipeline semantics and operator transformations interactively while debugging. This allows the effort to be proportional to the need, and to focus on the portions of the pipeline under the greatest suspicion. We present a generic debugger for running post-execution analysis of any IE pipeline consisting of arbitrary types of operators. For this, we propose an effective provenance model for IE pipelines which captures a variety of operator types, ranging from those for which full to no specifications are available. We have evaluated our proposed algorithms and provenance model on large-scale real-world extraction pipelines.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"49 1","pages":"2229-2232"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90660444","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}
Sentiment detection analyzes the positive or negative polarity of text. The field has received considerable attention in recent years, since it plays an important role in providing means to assess user opinions regarding an organization's products, services, or actions. Approaches towards sentiment detection include machine learning techniques as well as computationally less expensive methods. Both approaches rely on the use of language-specific sentiment lexicons, which are lists of sentiment terms with their corresponding sentiment value. The effort involved in creating, customizing, and extending sentiment lexicons is considerable, particularly if less common languages and domains are targeted without access to appropriate language resources. This paper proposes a semi-automatic approach for the creation of sentiment lexicons which assigns sentiment values to sentiment terms via crowd-sourcing. Furthermore, it introduces a bootstrapping process operating on unlabeled domain documents to extend the created lexicons, and to customize them according to the particular use case. This process considers sentiment terms as well as sentiment indicators occurring in the discourse surrounding a articular topic. Such indicators are associated with a positive or negative context in a particular domain, but might have a neutral connotation in other domains. A formal evaluation shows that bootstrapping considerably improves the method's recall. Automatically created lexicons yield a performance comparable to professionally created language resources such as the General Inquirer.
{"title":"Using games with a purpose and bootstrapping to create domain-specific sentiment lexicons","authors":"A. Weichselbraun, Stefan Gindl, A. Scharl","doi":"10.1145/2063576.2063729","DOIUrl":"https://doi.org/10.1145/2063576.2063729","url":null,"abstract":"Sentiment detection analyzes the positive or negative polarity of text. The field has received considerable attention in recent years, since it plays an important role in providing means to assess user opinions regarding an organization's products, services, or actions. Approaches towards sentiment detection include machine learning techniques as well as computationally less expensive methods. Both approaches rely on the use of language-specific sentiment lexicons, which are lists of sentiment terms with their corresponding sentiment value. The effort involved in creating, customizing, and extending sentiment lexicons is considerable, particularly if less common languages and domains are targeted without access to appropriate language resources. This paper proposes a semi-automatic approach for the creation of sentiment lexicons which assigns sentiment values to sentiment terms via crowd-sourcing. Furthermore, it introduces a bootstrapping process operating on unlabeled domain documents to extend the created lexicons, and to customize them according to the particular use case. This process considers sentiment terms as well as sentiment indicators occurring in the discourse surrounding a articular topic. Such indicators are associated with a positive or negative context in a particular domain, but might have a neutral connotation in other domains. A formal evaluation shows that bootstrapping considerably improves the method's recall. Automatically created lexicons yield a performance comparable to professionally created language resources such as the General Inquirer.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"59 1","pages":"1053-1060"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91015992","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}
Hongliang Fei, R. Jiang, Yuhao Yang, Bo Luo, Jun Huan
Information Flow Studies analyze the principles and mechanisms of social information distribution and is an essential research topic in social networks. Traditional approaches are primarily based on the social network graph topology. However, topology itself can not accurately reflect the user interests or activities. In this paper, we adopt a "microeconomics" approach to study social information diffusion and aim to answer the question that how social information flow and socialization behaviors are related to content similarity and user interests. In particular, we study content-based social activity prediction, i.e., to predict a user's response (e.g. comment or like) to their friends' postings (e.g. blogs) w.r.t. message content. In our solution, we cast the social behavior prediction problem as a multi-task learning problem, in which each task corresponds to a user. We have designed a novel multi-task learning algorithm that is specifically designed for learning information flow in social networks. In our model, we apply l1 and Tikhonov regularization to obtain a sparse and smooth model in a linear multi-task learning framework. Using comprehensive experimental study, we have demonstrated the effectiveness of the proposed learning method.
{"title":"Content based social behavior prediction: a multi-task learning approach","authors":"Hongliang Fei, R. Jiang, Yuhao Yang, Bo Luo, Jun Huan","doi":"10.1145/2063576.2063719","DOIUrl":"https://doi.org/10.1145/2063576.2063719","url":null,"abstract":"Information Flow Studies analyze the principles and mechanisms of social information distribution and is an essential research topic in social networks. Traditional approaches are primarily based on the social network graph topology. However, topology itself can not accurately reflect the user interests or activities. In this paper, we adopt a \"microeconomics\" approach to study social information diffusion and aim to answer the question that how social information flow and socialization behaviors are related to content similarity and user interests. In particular, we study content-based social activity prediction, i.e., to predict a user's response (e.g. comment or like) to their friends' postings (e.g. blogs) w.r.t. message content. In our solution, we cast the social behavior prediction problem as a multi-task learning problem, in which each task corresponds to a user. We have designed a novel multi-task learning algorithm that is specifically designed for learning information flow in social networks. In our model, we apply l1 and Tikhonov regularization to obtain a sparse and smooth model in a linear multi-task learning framework. Using comprehensive experimental study, we have demonstrated the effectiveness of the proposed learning method.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"6 1","pages":"995-1000"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91165521","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}
Information diffusion in social networks provide great opportunities for political and social change as well as societal education. Therefore understanding information diffusion in social networks is a critical research goal. This greater understanding can be achieved through data analysis, development of reliable models that can predict outcomes of social processes, and ultimately the creation of applications that can shape the outcome of these processes. In this tutorial, we aim to provide an overview of such recent research based on a wide variety of techniques such as optimization algorithms, data mining, data streams covering a large number of problems such as influence spread maximization, misinformation limitation and study of trends in online social networks.
{"title":"Information diffusion in social networks: observing and affecting what society cares about","authors":"D. Agrawal, Ceren Budak, A. E. Abbadi","doi":"10.1145/2063576.2064036","DOIUrl":"https://doi.org/10.1145/2063576.2064036","url":null,"abstract":"Information diffusion in social networks provide great opportunities for political and social change as well as societal education. Therefore understanding information diffusion in social networks is a critical research goal. This greater understanding can be achieved through data analysis, development of reliable models that can predict outcomes of social processes, and ultimately the creation of applications that can shape the outcome of these processes. In this tutorial, we aim to provide an overview of such recent research based on a wide variety of techniques such as optimization algorithms, data mining, data streams covering a large number of problems such as influence spread maximization, misinformation limitation and study of trends in online social networks.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"76 1","pages":"2609-2610"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90912052","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}
Relevance feedback is an effective approach to improve retrieval quality over the initial query. Typical relevance feedback methods usually select top-ranked documents for relevance judgments, then query expansion or model updating are carried out based on the feedback documents. However, the number of feedback documents is usually limited due to expensive human labeling. Thus relevant documents in the feedback set are hardly representative of all relevant documents and the feedback set is actually biased. As a result, the performance of relevance feedback will get hurt. In this paper, we first show how and where the bias problem exists through experiments. Then we study how the bias can be reduced by utilizing the unlabeled documents. After analyzing the usefulness of a document to relevance feedback, we propose an approach that extends the feedback set with carefully selected unlabeled documents by heuristics. Our experiment results show that the extended feedback set has less bias than the original feedback set and better performance can be achieved when the extended feedback set is used for relevance feedback.
{"title":"On bias problem in relevance feedback","authors":"Qianli Xing, Yi Zhang, Lanbo Zhang","doi":"10.1145/2063576.2063866","DOIUrl":"https://doi.org/10.1145/2063576.2063866","url":null,"abstract":"Relevance feedback is an effective approach to improve retrieval quality over the initial query. Typical relevance feedback methods usually select top-ranked documents for relevance judgments, then query expansion or model updating are carried out based on the feedback documents. However, the number of feedback documents is usually limited due to expensive human labeling. Thus relevant documents in the feedback set are hardly representative of all relevant documents and the feedback set is actually biased. As a result, the performance of relevance feedback will get hurt. In this paper, we first show how and where the bias problem exists through experiments. Then we study how the bias can be reduced by utilizing the unlabeled documents. After analyzing the usefulness of a document to relevance feedback, we propose an approach that extends the feedback set with carefully selected unlabeled documents by heuristics. Our experiment results show that the extended feedback set has less bias than the original feedback set and better performance can be achieved when the extended feedback set is used for relevance feedback.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"15 1","pages":"1965-1968"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89579408","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}
Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, I. Dhillon
We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship with other users as positive or negative. The signed links thus created reflect social attitudes of the users towards each other in terms of friendship or trust. Our first contribution is to show how any quantitative measure of social imbalance in a network can be used to derive a link prediction algorithm. Our framework allows us to reinterpret some existing algorithms as well as derive new ones. Second, we extend the approach of Leskovec et al. (2010) by presenting a supervised machine learning based link prediction method that uses features derived from longer cycles in the network. The supervised method outperforms all previous approaches on 3 networks drawn from sources such as Epinions, Slashdot and Wikipedia. The supervised approach easily scales to these networks, the largest of which has 132k nodes and 841k edges. Most real-world networks have an overwhelmingly large proportion of positive edges and it is therefore easy to get a high overall accuracy at the cost of a high false positive rate. We see that our supervised method not only achieves good accuracy for sign prediction but is also especially effective in lowering the false positive rate.
{"title":"Exploiting longer cycles for link prediction in signed networks","authors":"Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, I. Dhillon","doi":"10.1145/2063576.2063742","DOIUrl":"https://doi.org/10.1145/2063576.2063742","url":null,"abstract":"We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship with other users as positive or negative. The signed links thus created reflect social attitudes of the users towards each other in terms of friendship or trust. Our first contribution is to show how any quantitative measure of social imbalance in a network can be used to derive a link prediction algorithm. Our framework allows us to reinterpret some existing algorithms as well as derive new ones. Second, we extend the approach of Leskovec et al. (2010) by presenting a supervised machine learning based link prediction method that uses features derived from longer cycles in the network. The supervised method outperforms all previous approaches on 3 networks drawn from sources such as Epinions, Slashdot and Wikipedia. The supervised approach easily scales to these networks, the largest of which has 132k nodes and 841k edges. Most real-world networks have an overwhelmingly large proportion of positive edges and it is therefore easy to get a high overall accuracy at the cost of a high false positive rate. We see that our supervised method not only achieves good accuracy for sign prediction but is also especially effective in lowering the false positive rate.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"51 1","pages":"1157-1162"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89417575","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 propose a new leaning method for Multiple Kernel Learning (MKL) based on the upper bounds of the leave-one-out error that is an almost unbiased estimate of the expected generalization error. Specifically, we first present two new formulations for MKL by minimizing the upper bounds of the leave-one-out error. Then, we compute the derivatives of these bounds and design an efficient iterative algorithm for solving these formulations. Experimental results show that the proposed method gives better accuracy results than that of both SVM with the uniform combination of basis kernels and other state-of-art kernel learning approaches.
{"title":"Learning kernels with upper bounds of leave-one-out error","authors":"Yong Liu, Shizhong Liao, Yuexian Hou","doi":"10.1145/2063576.2063927","DOIUrl":"https://doi.org/10.1145/2063576.2063927","url":null,"abstract":"We propose a new leaning method for Multiple Kernel Learning (MKL) based on the upper bounds of the leave-one-out error that is an almost unbiased estimate of the expected generalization error. Specifically, we first present two new formulations for MKL by minimizing the upper bounds of the leave-one-out error. Then, we compute the derivatives of these bounds and design an efficient iterative algorithm for solving these formulations. Experimental results show that the proposed method gives better accuracy results than that of both SVM with the uniform combination of basis kernels and other state-of-art kernel learning approaches.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"149 1","pages":"2205-2208"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76751948","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}
Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management
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