2016 International Joint Conference on Neural Networks (IJCNN)最新文献_第8页

Simulating robotic cars using time-delay neural networks 用延时神经网络模拟机器人汽车

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727342

A. D. Souza, Jacson Rodrigues Correia-Silva, Filipe Wall Mutz, C. Badue, Thiago Oliveira-Santos

{"title":"Simulating robotic cars using time-delay neural networks","authors":"A. D. Souza, Jacson Rodrigues Correia-Silva, Filipe Wall Mutz, C. Badue, Thiago Oliveira-Santos","doi":"10.1109/IJCNN.2016.7727342","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727342","url":null,"abstract":"In this paper, we propose a simulator for robotic cars based on two time-delay neural networks. These networks are intended to simulate the mechanisms that govern how a set of effort commands changes the car's velocity and the direction it is moving. The first neural network receives as input a temporal sequence of current and previous throttle and brake efforts, along with a temporal sequence of the previous car's velocities (estimated by the network), and outputs the velocity that the real car would reach in the next time interval given these inputs. The second neural network estimates the arctangent of curvature (a variable related to the steering wheel angle) that a real car would reach in the next time interval given a temporal sequence of current and previous steering efforts and previous arctangents of curvatures of the car estimated by the network. We evaluated the performance of our simulator using real-world datasets acquired using an autonomous robotic car. Experimental results showed that our simulator was able to simulate in real time how a set of efforts influences the car's velocity and arctangent of curvature. While navigating in a map of a real-world environment, our car simulator was able to emulate the velocity and arctangent of curvature of the real car with mean squared error of 2.2×10-3 (m/s)2 and 4.0×10-5 rad2, respectively.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"60 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128636299","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

Aggregating pixel-level prediction and cluster-level texton occurrence within superpixel voting for roadside vegetation classification 在超像素投票中聚合像素级预测和聚类级文本发生用于路边植被分类

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727614

Ligang Zhang, B. Verma, David R. B. Stockwell, Sujan Chowdhury

{"title":"Aggregating pixel-level prediction and cluster-level texton occurrence within superpixel voting for roadside vegetation classification","authors":"Ligang Zhang, B. Verma, David R. B. Stockwell, Sujan Chowdhury","doi":"10.1109/IJCNN.2016.7727614","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727614","url":null,"abstract":"Roadside vegetation classification has recently attracted increasing attention, due to its significance in applications such as vegetation growth management and fire hazard identification. Existing studies primarily focus on learning visible feature based classifiers or invisible feature based thresholds, which often suffer from a generalization problem to new data. This paper proposes an approach that aggregates pixel-level supervised classification and cluster-level texton occurrence within a voting strategy over superpixels for vegetation classification, which takes into account both generic features in the training data and local characteristics in the testing data. Class-specific artificial neural networks are trained to predict class probabilities for all pixels, while a texton based adaptive K-means clustering process is introduced to group pixels into clusters and obtain texton occurrence. The pixel-level class probabilities and cluster-level texton occurrence are further integrated in superpixel-level voting to assign each superpixel to a class category. The proposed approach outperforms previous approaches on a roadside image dataset collected by the Department of Transport and Main Roads, Queensland, Australia, and achieves state-of-the-art performance using low-resolution images from the Croatia roadside grass dataset.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127439327","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

Filterbank learning for deep neural network based polyphonic sound event detection 基于深度神经网络的复音事件检测的滤波器库学习

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727634

Emre Çakir, Ezgi C. Ozan, T. Virtanen

{"title":"Filterbank learning for deep neural network based polyphonic sound event detection","authors":"Emre Çakir, Ezgi C. Ozan, T. Virtanen","doi":"10.1109/IJCNN.2016.7727634","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727634","url":null,"abstract":"Deep learning techniques such as deep feedforward neural networks and deep convolutional neural networks have recently been shown to improve the performance in sound event detection compared to traditional methods such as Gaussian mixture models. One of the key factors of this improvement is the capability of deep architectures to automatically learn higher levels of acoustic features in each layer. In this work, we aim to combine the feature learning capabilities of deep architectures with the empirical knowledge of human perception. We use the first layer of a deep neural network to learn a mapping from a high-resolution magnitude spectrum to smaller amount of frequency bands, which effectively learns a filterbank for the sound event detection task. We initialize the first hidden layer weights to match with the perceptually motivated mel filterbank magnitude response. We also integrate this initialization scheme with context windowing by using an appropriately constrained deep convolutional neural network. The proposed method does not only result with better detection accuracy, but also provides insight on the frequencies deemed essential for better discrimination of given sound events.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129991319","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: 30

Echo-state conditional variational autoencoder for anomaly detection 回声状态条件变分自编码器异常检测

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727309

Suwon Suh, Daniel H. Chae, Hyon-Goo Kang, Seungjin Choi

引用次数: 62

Tackling the ordinal and imbalance nature of a melanoma image classification problem 处理黑色素瘤图像分类问题的顺序和不平衡性质

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727466

M. Pérez-Ortiz, A. Sáez, J. Sánchez-Monedero, Pedro Antonio Gutiérrez, C. Hervás‐Martínez

{"title":"Tackling the ordinal and imbalance nature of a melanoma image classification problem","authors":"M. Pérez-Ortiz, A. Sáez, J. Sánchez-Monedero, Pedro Antonio Gutiérrez, C. Hervás‐Martínez","doi":"10.1109/IJCNN.2016.7727466","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727466","url":null,"abstract":"Melanoma is a type of cancer that usually occurs on the skin. Early detection is crucial for ensuring five-year survival (which varies between 15% and 99% depending on the melanoma stage). Melanoma severity is typically diagnosed by invasive methods (e.g. a biopsy). In this paper, we propose an alternative system combining image analysis and machine learning for detecting melanoma presence and severity. The 86 features selected consider the shape, colour, pigment network and texture of the melanoma. As opposed to previous studies that have focused on distinguishing melanoma and non-melanoma images, our work considers a finer-grain classification problem using five categories: benign lesions and 4 different stages of melanoma. The dataset presents two main characteristics that are approached by specific machine learning methods: 1) the classes representing melanoma severity follow a natural order, and 2) the dataset is imbalanced, where benign lesions clearly outnumber melanoma ones. Different nominal and ordinal classifiers are considered, one of them being based on an ordinal cascade decomposition method. The cascade method is shown to obtain good performance for all classes, while respecting and exploiting the order information. Moreover, we explore the alternative of applying a class balancing technique, presenting good synergy with the ordinal and nominal methods.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128949212","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

Towards emotion-based reputation guessing learning agents 基于情绪的声誉猜测学习代理

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727690

Jones Granatyr, J. P. Barddal, Adriano Weihmayer Almeida, F. Enembreck, Adaiane Pereira dos Santos Granatyr

引用次数: 2

Pain recognition and intensity classification using facial expressions 基于面部表情的疼痛识别和强度分类

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727659

W. A. Shier, S. Yanushkevich

引用次数: 10

Training deep neural networks on imbalanced data sets 在不平衡数据集上训练深度神经网络

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727770

Shoujin Wang, Wei Liu, Jia Wu, Longbing Cao, Qinxue Meng, Paul J. Kennedy

引用次数: 330

A study of an incremental spectral meta-learner for nonstationary environments 非平稳环境下增量谱元学习器的研究

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727178

G. Ditzler

{"title":"A study of an incremental spectral meta-learner for nonstationary environments","authors":"G. Ditzler","doi":"10.1109/IJCNN.2016.7727178","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727178","url":null,"abstract":"Incrementally learning from large volumes of streaming data over time is a problem that is of crucial importance to the computational intelligence community, especially in scenarios where it is impractical or simply unfeasible to store all historical data. Learning becomes a particularly challenging problem when the probabilistic properties of the data are changing with time (i.e., gradual, abrupt, etc.), and there is scarce availability of class labels. Many existing strategies for learning in nonstationary environments use the most recent batch of training data to tune their parameters (e.g., calculate classifier voting weights), and never reassess these parameters when the unlabeled test data arrive. Making a limited drift assumption is generally one way to justify not needing to re-evaluate the parameters of a classifiers; however, labeled data that have already been learned if presented to the classifier for testing could be forgotten because the data was not observed for a long time. This is one form of abrupt concept drift with unlabeled data. In this work, an incremental spectral learning meta-classifier is presented for learning in nonstationary environments such that: (i) new classifiers can be added into an ensemble when labeled data are available, (ii) the ensemble voting weights are determined from the unlabeled test data to boost recollection of previously learned distributions of data, and (iii) the limited drift assumption is removed from the test-then-train evaluation paradigm. We benchmark our proposed approach on several widely used concept drift data sets.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130904435","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

Extended Kalman filter under maximum correntropy criterion 最大熵准则下的扩展卡尔曼滤波

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727408

Xi Liu, Hua Qu, Ji-hong Zhao, Badong Chen

引用次数: 56