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2007 International Joint Conference on Neural Networks最新文献

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Online Learning for Hierarchical Networks of Locally Arranged Models using a Support Vector Domain Model 基于支持向量域模型的局部排列模型分层网络在线学习
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370966
F. Hoppe, G. Sommer
{"title":"Online Learning for Hierarchical Networks of Locally Arranged Models using a Support Vector Domain Model","authors":"F. Hoppe, G. Sommer","doi":"10.1109/IJCNN.2007.4370966","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370966","url":null,"abstract":"We propose two new developments for our supervised local linear approximation technique, the so called Hierarchical Network of Locally Arranged Models. A new model will be presented that defines those local regions of the input space in which linear models are trained to approximate the target function. This model is based on a one-class support vector machine and helps to improve the approximation quality. Secondly, an online learning algorithm for our approach will be described that can be used in applications where training data is only available as a continuous stream of samples. It allows to adapted a network to a function that may change over time. The success of these two developments is proven with three benchmark tests.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121834676","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}
引用次数: 1
Selecting the Most Influential Nodes in Social Networks 选择社会网络中最具影响力的节点
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371333
PabloA . Estevez, Pablo A. Vera, Kazumi Saito
{"title":"Selecting the Most Influential Nodes in Social Networks","authors":"PabloA . Estevez, Pablo A. Vera, Kazumi Saito","doi":"10.1109/IJCNN.2007.4371333","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371333","url":null,"abstract":"A set covering greedy algorithm is proposed for solving the influence maximization problem in social networks. Two information diffusion models are considered: Independent Cascade Model and Linear Threshold Model. The proposed algorithm is compared with traditional maximization algorithms such as simple greedy and degree centrality using three data sets. In addition, an algorithm for mapping social networks is proposed, which allows visualizing the infection process and how the different algorithms evolve. The proposed approach is useful for mining large social networks.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117059292","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}
引用次数: 77
A New Method of Solving Permutation Problem in Blind Source Separation for Convolutive Acoustic Signals in Frequency-domain 一种解决频域卷积声信号盲源分离中置换问题的新方法
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371135
Wenyan Wu, Liming Zhang
{"title":"A New Method of Solving Permutation Problem in Blind Source Separation for Convolutive Acoustic Signals in Frequency-domain","authors":"Wenyan Wu, Liming Zhang","doi":"10.1109/IJCNN.2007.4371135","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371135","url":null,"abstract":"This paper proposes a novel scheme to solve permutation ambiguity in frequency-domain for the separation of the convolutive mixing signals. We use sparseness of original acoustic signals to build a histogram of direction of arrival (DOA) for the original signals, and then use mask technique to get rough recovered signals with distortion but no order problem. An independent component analysis (ICA) is implemented to solve more accurate separation at each frequency bin. The permutation problem can easily be solved based on the rough recovered signals by mask of DOA histogram. Compared with the existing algorithms, the proposed algorithm has better performance than both ICA and time-frequency mask methods.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"233 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121088383","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}
引用次数: 1
A Novel Automatic Framework for Scoliosis X-Ray Image Retrieval 一种新的脊柱侧凸x线图像自动检索框架
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371348
Zhiping Xu, Jinhong Pan, Shiyong Zhang
{"title":"A Novel Automatic Framework for Scoliosis X-Ray Image Retrieval","authors":"Zhiping Xu, Jinhong Pan, Shiyong Zhang","doi":"10.1109/IJCNN.2007.4371348","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371348","url":null,"abstract":"The paper proposed a novel automatic scoliosis X-ray image retrieval framework based on the global statistical feature of edge, edge co-occurrence matrix (ECM) and the local geometrical feature set of the whole spine, angle of each spine curve. The ECM is based on the statistical feature attained from the edge detection operators which applied on the image. The eigenvectors obtained from principle component analysis (PCA) of the ECM can preserve the high spatial frequencies components, so they are well suited for shape as well as texture representation. The geometrical feature like the Cobb's angle of each spine curve could be derived from the image segmentation based on the Intersecting Cortical Model, which is elicitation of the Eckhorn's model. The experiment shows that the framework shows good accuracy for the input query X-ray image in our work.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127131533","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}
引用次数: 6
Incremental Learning for Text Document Classification 文本文档分类的增量学习
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371367
ZhiHang Chen, Liping Huang, Y. Murphey
{"title":"Incremental Learning for Text Document Classification","authors":"ZhiHang Chen, Liping Huang, Y. Murphey","doi":"10.1109/IJCNN.2007.4371367","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371367","url":null,"abstract":"This paper presents our research in incremental learning for text document classification. Incremental learning is important in text document classification since many applications have huge amount of training data, and training documents become available through time. We propose an incremental learning framework, ILTC(Incremental Learning of Text Classification) that involves the learning of features of text classes followed by an incremental Perceptron learning process. ILTC has the capabilities of incremental learning of new feature dimensions as well as new document classes. We applied the ILTC to a classification system of diagnostic text documents. The experiment results demonstrate that ILTC was able to incrementally learn new knowledge from newly available training data without either referring to the older training data or forgetting the already learnt knowledge.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125086701","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}
引用次数: 20
Warranty Cost Forecast Based on Car Failure Data 基于汽车故障数据的保修成本预测
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370939
T. Hrycej, M. Grabert
{"title":"Warranty Cost Forecast Based on Car Failure Data","authors":"T. Hrycej, M. Grabert","doi":"10.1109/IJCNN.2007.4370939","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370939","url":null,"abstract":"A failure and warranty cost model is gained from a failure database. The model is a combination of statistical components with a multi-layer perceptron and a cross-entropy based learning rule. The model is used for forecasting warranty costs in alternative warranty condition scenarios. The estimate of forecast variance considers both the individual vehicle risk and the overall manufacturing quality fluctuation risk.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126068816","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}
引用次数: 10
Object Recognition with Generic Self Organizing Feature Extractors and Fast Gabor Wavelet Transform 基于通用自组织特征提取器和快速Gabor小波变换的目标识别
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370967
H. Ozer, R. Sundaram
{"title":"Object Recognition with Generic Self Organizing Feature Extractors and Fast Gabor Wavelet Transform","authors":"H. Ozer, R. Sundaram","doi":"10.1109/IJCNN.2007.4370967","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370967","url":null,"abstract":"This paper presents a biologically inspired object recognition algorithm that is tolerant to two dimensional (2D) affine transformations such as scaling and translation in the image plane and three-dimensional (3D) transformations of an object such as illumination changes and rotation in depth. The algorithm achieves this goal by extracting object features using Gabor wavelets and self-organizing maps in a hierarchical manner. Object features are learned in an unsupervised way which is consistent with the feature learning process in the visual cortex. The algorithm is analyzed for robustness. A support vector machine (SVM) classifier is used to test the classification efficiency of the algorithm.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123767083","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}
引用次数: 1
PSMS for Neural Networks on the IJCNN 2007 Agnostic vs Prior Knowledge Challenge IJCNN 2007不可知论与先验知识挑战赛上神经网络的PSMS
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371038
H. J. Escalante, M. Montes-y-Gómez, L. Sucar
{"title":"PSMS for Neural Networks on the IJCNN 2007 Agnostic vs Prior Knowledge Challenge","authors":"H. J. Escalante, M. Montes-y-Gómez, L. Sucar","doi":"10.1109/IJCNN.2007.4371038","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371038","url":null,"abstract":"Artificial neural networks have been proven to be effective learning algorithms since their introduction. These methods have been widely used in many domains, including scientific, medical, and commercial applications with great success. However, selecting the optimal combination of preprocessing methods and hyperparameters for a given data set is still a challenge. Recently a method for supervised learning model selection has been proposed: Particle Swarm Model Selection (PSMS). PSMS is a reliable method for the selection of optimal learning algorithms together with preprocessing methods, as well as for hyperparameter optimization. In this paper we applied PSMS for the selection of the (pseudo) optimal combination of preprocessing methods and hyperparameters for a fixed neural network on benchmark data sets from a challenging competition: the (IJCNN 2007) agnostic vs prior knowledge challenge. A forum for the evaluation of methods for model selection and data representation discovery. In this paper we further show that the use of PSMS is useful for model selection when we have no knowledge about the domain we are dealing with. With PSMS we obtained competitive models that are ranked high in the official results of the challenge.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123780926","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}
引用次数: 12
A Model of the Subthalamo-Pallidal Network Undergoing Extrinsic Stimulation 受外部刺激的丘脑下-苍白质网络模型
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371436
S. Leondopulos, E. Micheli-Tzanakou
{"title":"A Model of the Subthalamo-Pallidal Network Undergoing Extrinsic Stimulation","authors":"S. Leondopulos, E. Micheli-Tzanakou","doi":"10.1109/IJCNN.2007.4371436","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371436","url":null,"abstract":"A model of the subthalamo-pallidal loop using integrate-and-fire neurons in conjunction with first order models of synaptic afferents, calcium and potassium currents, and a leaky sodium current exhibits activity similar to that recorded in the Subthalamic nucleus of Parkinson patients undergoing stereotactic neurosurgery and deep brain stimulation (DBS). In particular, by incorporating a negative feedback loop, the model produces inhibitory responses to individual DBS pulses and exhibits oscillations in the alpha and beta range (8-29 Hz). Moreover, an increase in either DBS stimulus energy or GPe spontaneous activity causes the oscillatory behavior to subside.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125485858","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}
引用次数: 1
Class-modular multi-layer perceptions, task decomposition and virtually balanced training subsets 类模块化多层感知，任务分解和几乎平衡的训练子集
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371291
G. Daqi, Wang Wei, Gao Jianliang
{"title":"Class-modular multi-layer perceptions, task decomposition and virtually balanced training subsets","authors":"G. Daqi, Wang Wei, Gao Jianliang","doi":"10.1109/IJCNN.2007.4371291","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371291","url":null,"abstract":"This paper focuses on how to use class-modular single-hidden-layer perceptrons (MLPs) with sigmoid activation functions (SAFs) to solve the multi-class learning problems, and pays special attention to the unbalanced data sets. Our solutions are as follows. (A) An n-class learning problem first decomposes into n two-class problems (B) A single-output MLP is responsible for solving a two-class problem, separating its represented class with all the other classes, and trained only by the samples from the represented class and some neighboring ones. (C) The samples from the minority classes or in the thin regions are virtually reinforced (D)The generalization region of an MLP is localized. The proposed method is verified effective by the experimental result of letter recognition.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125560907","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}
引用次数: 1
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