发布求助
文献互助 智能选刊 最新文献

2007 International Joint Conference on Neural Networks最新文献

筛选
英文 中文
Feature/Model Selection by the Linear Programming SVM Combined with State-of-Art Classifiers: What Can We Learn About the Data 结合最新分类器的线性规划支持向量机特征/模型选择:我们能从数据中学到什么
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371201
Erinija Pranckevičienė, R. Somorjai, M. Tran
{"title":"Feature/Model Selection by the Linear Programming SVM Combined with State-of-Art Classifiers: What Can We Learn About the Data","authors":"Erinija Pranckevičienė, R. Somorjai, M. Tran","doi":"10.1109/IJCNN.2007.4371201","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371201","url":null,"abstract":"Many real-world classification problems are represented by very sparse and high-dimensional data. The recent successes of a linear programming support vector machine (LPSVM) for feature selection motivated a deeper analysis of the method when applied to sparse, multivariate data. Due to the sparseness, the selection of a classification model is greatly influenced by the characteristics of that particular dataset. In this study, we investigate a feature selection strategy based on LPSVM as the initial feature filter, combined with state-of-art classification rules, and apply to five real-life datasets of the agnostic learning vs. prior knowledge challenge of IJCNN2007. Our goal is to better understand the robustness of LPSVM as a feature filter. Our analysis suggests that LPSVM can be a useful black box method for identification of the profile of the informative features in the data. If the data are complex and better separable by nonlinear methods, then feature pre-filtering by LPSVM enhances the data representation for other classifiers.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"24 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":"134426303","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: 13
Long Term Prediction of Chaotic Time Series with the Aid of Neuro Fuzzy Models, Spectral Analysis and Correlation Analysis 基于神经模糊模型、谱分析和相关分析的混沌时间序列长期预测
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371229
M. Mirmomeni, C. Lucas, B. Moshiri
{"title":"Long Term Prediction of Chaotic Time Series with the Aid of Neuro Fuzzy Models, Spectral Analysis and Correlation Analysis","authors":"M. Mirmomeni, C. Lucas, B. Moshiri","doi":"10.1109/IJCNN.2007.4371229","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371229","url":null,"abstract":"This paper presents a novel methodology for long term prediction of chaotic time series based on spectral analysis and neuro fuzzy modeling. A main motivation of using spectral analysis is to find some long term predictable components which describe the time series dynamics properly. In addition, this paper proposes a novel input variables selection criterion which is based on correlation analysis. The objective of this algorithm is to maximize relevance between inputs and output and minimizes the redundancy of selected inputs. After selecting input variables, a locally linear neuro fuzzy model is optimized for each of the principal components obtained from singular spectrum analysis, and the multi step predicted values are recombined to make the natural chaotic phenomenon. Two case studies are considered in this paper. The method has been applied to the long-term prediction of disturbance storm time (DST) as a solar activity indexes and one time series from neural forecasting competitions NN3. Results depict the power of the proposed method in long-term prediction of chaotic time series.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"111 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":"134307452","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: 5
A Fuzzy Approach to Stereo Vision Using Pyramidal Images with Different Starting Level 基于不同起始层次金字塔图像的立体视觉模糊算法
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371423
Marcos D. Medeiros, L. Gonçalves
{"title":"A Fuzzy Approach to Stereo Vision Using Pyramidal Images with Different Starting Level","authors":"Marcos D. Medeiros, L. Gonçalves","doi":"10.1109/IJCNN.2007.4371423","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371423","url":null,"abstract":"We propose a stereo matching algorithm based on multiresolution correlation that varies the depth for the resolution level with which to start stereo calculation for each image pixel (or block of pixels). The initial depth depends on the images local characteristics. We propose to use a neural fuzzy approach to calculate the desirable depth for each pixel of one of the matching images and then use this starting depth to proceed with the multiresolution approach. Variable depth correlation reduces the errors caused by coarse levels. At the same time, the new fuzzy heuristic that we propose for calculating the desired depth keeps most of the blocks at a coarse level, thus having little impact on execution time. Variable depth correlation is expected to have little problems with very plain surfaces and borders, but is rather faster than usual algorithms. In the tests, the multiresolution algorithm proposed here performed faster than plain correlation, with much better results","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"41 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":"127575334","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: 2
Neural Network Strategy for Sampling of Particle Filters on the Tracking Problem 跟踪问题中粒子滤波采样的神经网络策略
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370964
Zhongyu Pang, Derong Liu, N. Jin, Zhuo Wang
{"title":"Neural Network Strategy for Sampling of Particle Filters on the Tracking Problem","authors":"Zhongyu Pang, Derong Liu, N. Jin, Zhuo Wang","doi":"10.1109/IJCNN.2007.4370964","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370964","url":null,"abstract":"Sequential Monte Carlo methods, namely particle filters, are popular statistic techniques for sampling sequentially from a complex probability distribution. Sampling is a key step for particle filters and has vital effects on simulation results. Since degeneracy of particles in samples sometimes is very severe, there exist only a few particles with significant weights. Thus the sample diversity is reduced significantly so that only a few particles are used to represent the corresponding probability distribution. Therefore, resampling has to be used very often during the whole procedure. This paper addresses a new method which can avoid the phenomenon of particle degeneracy. A backpropagation neural network is used to adjust low weight particles in order to increase their weights and particles with high weights may be split into two small ones if needed. Our simulation results on a typical tracking problem show that not only the phenomenon of particle degeneracy is effectively avoided but also tracking results are much better than those of the traditional particle filter.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"6 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":"132598725","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
Evolving a Neural Net-Based Decision and Search Heuristic for DPLL SAT Solvers 一种基于神经网络的DPLL SAT求解决策和搜索启发式算法
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371054
Raihan H. Kibria
{"title":"Evolving a Neural Net-Based Decision and Search Heuristic for DPLL SAT Solvers","authors":"Raihan H. Kibria","doi":"10.1109/IJCNN.2007.4371054","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371054","url":null,"abstract":"Solvers for the Boolean satisfiability problem are an important base technology for many applications. The most efficient SAT solvers for industrial applications are based on the DPLL algorithm with clause learning and conflict analysis dependent decision heuristics. The solver MINISAT V1.14 was modified to use a neural-net-based decision heuristic and search strategy. The weights and biases of the multilayer feedforward neural net are generated by an evolution strategy which is trained on a sample set of SAT problems. Problems solved with the evolved solutions encounter a similar number of conflicts as the original program, but require a higher number of decisions.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"29 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":"129389706","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: 3
Neural Networks for Complex Valued Signals: A Preliminary Study 复杂值信号神经网络的初步研究
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371317
S. Chandana
{"title":"Neural Networks for Complex Valued Signals: A Preliminary Study","authors":"S. Chandana","doi":"10.1109/IJCNN.2007.4371317","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371317","url":null,"abstract":"This article presents the work related to the design and architecture of a special neural network capable of dealing effectively with Complex numbers. The proposed architecture employs parameter space partitioning and a novel partition mapping scheme. An empirical design based partially on the concepts of Rough Sets has been described. The applied signal in the form of Complex numbers is divided into a set (containing both the imaginary and real coefficients) and, a subset (containing of only the real coefficient). These set-subsets are processed by specialized neurons. The proposed architecture displays superior learning speeds and similar accuracy when compared to other established complex-valued-neural-networks.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"23 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":"126589619","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: 0
A Hierarchical Generative Model for Overcomplete Topographic Representations in Natural Images 自然图像中过完备地形表示的层次生成模型
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371128
Libo Ma, Liqing Zhang
{"title":"A Hierarchical Generative Model for Overcomplete Topographic Representations in Natural Images","authors":"Libo Ma, Liqing Zhang","doi":"10.1109/IJCNN.2007.4371128","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371128","url":null,"abstract":"In this paper we propose a hierarchical generative model based on sparse coding and analysis of topographic energy dependencies. We further formulate the basic sparse coding into a hierarchical fashion by defining a higher-order topography on the coefficients of nearby basis functions. An algorithm for learning overcomplete topographic basis functions is derived from a direct approximation to the data likelihood. The basis functions learned by the algorithm demonstrate the topographic organization and the emergence of phase-and shift-invariant features - the similar properties of visual complex cells. Moreover, the proposed model yields overcomplete representations. We apply the model to the problem of image denoising. This task suits the model well since Gaussian additive noise is explicitly included in the model. The simulation results suggest that the proposed method outperforms conventional denoising algorithms. Our model is promising in a wide range of fields, such as signal processing and pattern recognition.","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":"131067951","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: 8
Humanoid Robotics Modeling by Dynamic Fuzzy Neural Network 基于动态模糊神经网络的仿人机器人建模
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371377
Zhe Tang, M. Er, G. Ng
{"title":"Humanoid Robotics Modeling by Dynamic Fuzzy Neural Network","authors":"Zhe Tang, M. Er, G. Ng","doi":"10.1109/IJCNN.2007.4371377","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371377","url":null,"abstract":"Motion planning is an essential task for humanoid robots. However, it is still very challenging to obtain good motion performance in humanoid motion planning, because of its high DOFs (degree of freedoms), variable mechanical structure and nonlinearity. In humanoid motion planning, the motion performance can be given only after one whole cycle motion is completed. This is a demanding condition for motion planning on either real robots or simulation platform. In this paper, a DFNN (dynamic neural fuzzy network) is adopted to model humanoid robots for motion planning. The inputs of DFNN are parameters which determine motion of humanoid robots. The output is evaluation of humanoid motion performance. The DFNN after training can give evaluation of motion performance immediately once the parameters are determined. The DFNN models not only the dynamics of robots, also the motion planning method. Therefore, the DFNN stores two kind of knowledge: the mapping between parameters and humanoid motion, the mapping between humanoid motion and motion performance.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"1 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":"130935725","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: 5
Distance-based Disagreement Classifiers Combination 基于距离的分歧分类器组合
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371390
C. Freitas, J. Carvalho, José Josemar de Oliveira, S. B. K. Aires, R. Sabourin
{"title":"Distance-based Disagreement Classifiers Combination","authors":"C. Freitas, J. Carvalho, José Josemar de Oliveira, S. B. K. Aires, R. Sabourin","doi":"10.1109/IJCNN.2007.4371390","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371390","url":null,"abstract":"We present a methodology to analyze multiple classifiers systems (MCS) performance, using the diversity concept. The goal is to define an alternative approach to the conventional recognition rate criterion, which usually requires an exhaustive combination search. This approach defines a distance-based disagreement (DbD) measure using an Euclidean distance computed between confusion matrices and a soft-correlation rule to indicate the most likely candidates to the best classifiers ensemble. As case study, we apply this strategy to two different handwritten recognition systems. Experimental results indicate that the method proposed can be used as a low-cost alternative to conventional approaches.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"29 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":"133506313","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: 3
Inference of Genetic Networks using a Reduced NGnet Model 基于简化NGnet模型的遗传网络推理
2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371083
Shuhei Kimura, Katsuki Sonoda, S. Yamane, Kotaro Yoshida, Koki Matsumura, Mariko Okada
{"title":"Inference of Genetic Networks using a Reduced NGnet Model","authors":"Shuhei Kimura, Katsuki Sonoda, S. Yamane, Kotaro Yoshida, Koki Matsumura, Mariko Okada","doi":"10.1109/IJCNN.2007.4371083","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371083","url":null,"abstract":"The inference of genetic networks using a model based on a set of differential equations is generally time-consuming. In order to decrease its computational time, we have proposed the inference method using a normalized Gaussian network (NGnet) model. The inferred models however contain many false-positive regulations when we apply the NGnet approach to the genetic network inference problems. This paper proposes the reduced NGnet model and the gradual reduction strategy to overcome the drawbacks of the NGnet approach. Then, in order to verify their effectiveness, we apply the inference method using the proposed techniques to several artificial genetic network inference problems.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"125 9 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":"132151056","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: 4
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信