The 2011 International Joint Conference on Neural Networks最新文献_第9页

A Neural Network model for spatial mental imagery investigation: A study with the humanoid robot platform iCub 空间心理意象研究的神经网络模型——基于仿人机器人平台iCub的研究

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033501

A. D. Nuovo, D. Marocco, S. Nuovo, A. Cangelosi

{"title":"A Neural Network model for spatial mental imagery investigation: A study with the humanoid robot platform iCub","authors":"A. D. Nuovo, D. Marocco, S. Nuovo, A. Cangelosi","doi":"10.1109/IJCNN.2011.6033501","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033501","url":null,"abstract":"Understanding the process behind the human ability of creating mental images of events and experiences is a still crucial issue for psychologists. Mental imagery may be considered a multimodal biological simulation that activates the same, or very similar, sensorial and motor modalities that are activated when we interact with the environment in real time. Neuro-psychological studies show that neural mechanisms underlying real-time visual perception and mental visualization are the same when a task is mentally recalled. Nevertheless, the neural mechanisms involved in the active elaboration of mental images might be different from those involved in passive elaborations. The enhancement of this active and creative imagery is the aim of most psychological and educational processes, although, more empirical effort is needed in order to understand the mechanisms and the role of active mental imagery in human cognition. In this work we present some results of on ongoing investigation about mental imagery using cognitive robotics. Here we focus on the capability to estimate, from proprioceptive and visual information, the position into a soccer field when the robot acquires the goal. Results of simulation with the iCub platform are given to show that the computational model is able to efficiently estimate the robot's position. The final objective of our work is to replicate with a cognitive robotics model the mental imagery when it is used during the training phase of athletes that are allowed to imaginary practice to score a goal.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130693292","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

Filter Bank Common Spatial Pattern (FBCSP) algorithm using online adaptive and semi-supervised learning 采用在线自适应和半监督学习的滤波器组公共空间模式(FBCSP)算法

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033248

K. Ang, Z. Chin, Haihong Zhang, Cuntai Guan

{"title":"Filter Bank Common Spatial Pattern (FBCSP) algorithm using online adaptive and semi-supervised learning","authors":"K. Ang, Z. Chin, Haihong Zhang, Cuntai Guan","doi":"10.1109/IJCNN.2011.6033248","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033248","url":null,"abstract":"The Filter Bank Common Spatial Pattern (FBCSP) algorithm employs multiple spatial filters to automatically select key temporal-spatial discriminative EEG characteristics and the Naïve Bayesian Parzen Window (NBPW) classifier using offline learning in EEG-based Brain-Computer Interfaces (BCI). However, it has yet to address the non-stationarity inherent in the EEG between the initial calibration session and subsequent online sessions. This paper presents the FBCSP that employs the NBPW classifier using online adaptive learning that augments the training data with available labeled data during online sessions. However, employing semi-supervised learning that simply augments the training data with available data using predicted labels can be detrimental to the classification accuracy. Hence, this paper presents the FBCSP using online semi-supervised learning that augments the training data with available data that matches the probabilistic model captured by the NBPW classifier using predicted labels. The performances of FBCSP using online adaptive and semi-supervised learning are evaluated on the BCI Competition IV datasets IIa and IIb and compared to the FBCSP using offline learning. The results showed that the FBCSP using online semi-supervised learning yielded relatively better session-to-session classification results compared against the FBCSP using offline learning. The FBCSP using online adaptive learning on true labels yielded the best results in both datasets, but the FBCSP using online semi-supervised learning on predicted labels is more practical in BCI applications where the true labels are not available.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132311887","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: 31

Online incremental clustering with distance metric learning for high dimensional data 基于距离度量学习的高维数据在线增量聚类

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033478

S. Okada, T. Nishida

{"title":"Online incremental clustering with distance metric learning for high dimensional data","authors":"S. Okada, T. Nishida","doi":"10.1109/IJCNN.2011.6033478","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033478","url":null,"abstract":"In this paper, we present a novel incremental clustering algorithm which assigns of a set of observations into clusters and learns the distance metric iteratively in an incremental manner. The proposed algorithm SOINN-AML is composed based on the Self-organizing Incremental Neural Network (Shen et al 2006), which represents the distribution of unlabeled data and reports a reasonable number of clusters. SOINN adopts a competitive Hebbian rule for each input signal, and distance between nodes is measured using the Euclidean distance. Such algorithms rely on the distance metric for the input data patterns. Distance Metric Learning (DML) learns a distance metric for the high dimensional input space of data that preserves the distance relation among the training data. DML is not performed for input space of data in SOINN based approaches. SOINN-AML learns input space of data by using the Adaptive Distance Metric Learning (AML) algorithm which is one of the DML algorithms. It improves the incremental clustering performance of the SOINN algorithm by optimizing the distance metric in the case that input data space is high dimensional. In experimental results, we evaluate the performance by using two artificial datasets, seven real datasets from the UCI dataset and three real image datasets. We have found that the proposed algorithm outperforms conventional algorithms including SOINN (Shen et al 2006) and Enhanced SOINN (Shen et al 2007). The improvement of clustering accuracy (NMI) is between 0.03 and 0.13 compared to state of the art SOINN based approaches.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129208853","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: 17

Autonomous learning of a human body model 人体模型的自主学习

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033243

T. Walther, R. Würtz

引用次数: 2

Problems of temporal granularity in robot control: Levels of adaptation and a necessity of self-confidence 机器人控制中的时间粒度问题:适应水平和自信的必要性

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033568

H. Wagatsuma, Yousuke Tomonaga

{"title":"Problems of temporal granularity in robot control: Levels of adaptation and a necessity of self-confidence","authors":"H. Wagatsuma, Yousuke Tomonaga","doi":"10.1109/IJCNN.2011.6033568","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033568","url":null,"abstract":"The granularity of “action” within a system is highly depending on the internal representation for the task, or intention of what to do if it is a biological system. In the same time, there are several levels of adaptation when the system tries to complete a mission. The problem of choosing the right level of action representation is essential for robot controls as well as in learning paradigms. Both tend to use low-granularity and transfer the processed information to upper levels constructively. However the system never guarantees the completion time of the mission if the system is composed of stiff functional blocks with a specific temporal granularity at the bottom level. However, we biological system have an ability to manage the global time for scheduling and reorganization of tasks to finish by the deadline. Brain-inspired robotics allows us to investigate a distributed parallel information system, the brain, with the ability of time management as a real time control system of the physical body through flexible planning of necessary actions by interacting with the real environment. It is an extension of subsumption approaches that fixed a set of behaviors as the basic unit of action in the viewpoint of temporal property. By focusing on the temporal granularity as a consequence of coordination among multiple levels, a self-confident robot control may arise from a coupling between top-down or purpose-oriented decomposition of the purpose to primitive functions with flexible time windows and bottom-up of sensori-motor reactions in dynamic environments.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126806438","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

New approaches for solving permutation indeterminacy and scaling ambiguity in frequency domain separation of convolved mixtures 求解卷积混合频域分离中排列不确定性和标度模糊的新方法

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033319

Zhitang Chen, L. Chan

{"title":"New approaches for solving permutation indeterminacy and scaling ambiguity in frequency domain separation of convolved mixtures","authors":"Zhitang Chen, L. Chan","doi":"10.1109/IJCNN.2011.6033319","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033319","url":null,"abstract":"Permutation indeterminacy and scaling ambiguity occur in ICA and they are particularly problematic in time-frequency domain separation of convolutive mixtures. The quality of separation is severely degraded if these two problems are not well addressed. In this paper, we propose new approaches to solve the permutation indeterminacy and scaling ambiguity in the separation of convolutive mixture in frequency domain. We first apply Short Time Fourier Transform to the observed signals in order to transform the convolutive mixing in time domain to instantaneous mixing in time-frequency domain. A fixed-point algorithm with test of saddle point is adopted to derive the separated components in each frequency bin. To solve the permutation problem,we propose a new matching algorithm for this purpose. First we use discrete Haar Wavelet Transform to extract the feature vectors from the magnitude waveforms of the separated components and use Singular Value Decomposition to achieve dimension reduction. The permutation problem is solved by clustering the feature vectors using the new matching algorithm which is a combination of basic K-means and Hungarian algorithm. To solve the scaling ambiguity problem, we treat it as an overcomplete problem and realize it by maximizing the posterior of the scaling factor. Finally, experiments are conducted using benchmark data to present the effectiveness and performance of our proposed algorithms.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126237003","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

GA-based feature selection approach in biometric hand systems 基于遗传算法的手部生物识别系统特征选择方法

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033228

Rafael Marcos Luque Baena, D. Elizondo, Ezequiel López-Rubio, E. Palomo

引用次数: 12

Three theorems: Brain-like networks logically reason and optimally generalize 三个定理:类脑网络逻辑推理和最佳推广

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033613

J. Weng

{"title":"Three theorems: Brain-like networks logically reason and optimally generalize","authors":"J. Weng","doi":"10.1109/IJCNN.2011.6033613","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033613","url":null,"abstract":"Finite Automata (FA) is a base net for many sophisticated probability-based systems of artificial intelligence. However, an FA processes symbols, instead of images that the brain senses and produces (e.g., sensory images and motor images). Of course, many recurrent artificial neural networks process images. However, their non-calibrated internal states prevent generalization, let alone the feasibility of immediate and error-free learning. I wish to report a general-purpose Developmental Program (DP) for a new type of, brain-anatomy inspired, networks — Developmental Networks (DNs). The new theoretical results here are summarized by three theorems. (1) From any complex FA that demonstrates human knowledge through its sequence of the symbolic inputs-outputs, the DP incrementally develops a corresponding DN through the image codes of the symbolic inputs-outputs of the FA. The DN learning from the FA is incremental, immediate and error-free. (2) After learning the FA, if the DN freezes its learning but runs, it generalizes optimally for infinitely many image inputs and actions based on the embedded inner-product distance, state equivalence, and the principle of maximum likelihood. (3) After learning the FA, if the DN continues to learn and run, it “thinks” optimally in the sense of maximum likelihood based on its past experience.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126374445","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: 15

A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics 在移动机器人中使用相互关联和尖峰神经网络的声音定位技术的比较

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033468

J. Wall, T. McGinnity, L. Maguire

引用次数: 3

Selecting the hypothesis space for improving the generalization ability of Support Vector Machines 为提高支持向量机泛化能力选择假设空间

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033356

D. Anguita, A. Ghio, L. Oneto, S. Ridella

引用次数: 24