{"title":"A probabilistic DBNN with applications to sensor fusion and object recognition","authors":"Shang-Hung Lin, S. Kung, Long-Ji Lin","doi":"10.1109/NNSP.1995.514907","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514907","url":null,"abstract":"Given an input vector x, a classifier is supposed to tell which class is most likely to have produced it. Thus most data classifiers are designed to have K output nodes corresponding to K classes, {w/sub i/: i=1,...,K}. When pattern classes are clearly separated, this kind of data classifier usually performs very well. A specific model is the decision-based neural network (DBNN), which is effective in many signal/image classification applications. This is particularly the case when pattern classes are clearly separable. However, for those applications which have complex pattern distribution with two or more classes overlapping in pattern space, the traditional DBNN may not be effective or appropriate. For such applications, it is preferable to adopt a probabilistic classifier. In this paper, we develop a new probabilistic variant of the DBNN, which is meant for better estimate probability density functions corresponding to different pattern classes. For this purpose, new learning rules for probabilistic DBNN are derived. In experiments on face databases, we have observed noticeable improvement in various performance measures such as recognition accuracies and, in particular, false acceptance/rejection rates. Taking advantage of probabilistic output values of the DBNN, we construct a multiple sensor fusion system for object classification. In a sense, it represents an extension of the traditional hierarchical structure of DBNN.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129246325","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}
{"title":"Speaker verification using phoneme-based neural tree networks and phonetic weighting scoring method","authors":"Han-Sheng Liou, R. Mammone","doi":"10.1109/NNSP.1995.514895","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514895","url":null,"abstract":"A text-dependent speaker verification system based on neural tree network (NTN) phoneme model and phonetic weighting scoring method is presented. The system uses a set of concatenated NTNs trained on phonemes to model a password. In contrast to the conventional stochastic approaches which model the phonemes by hidden Markov models (HMMs), the new approach utilizes the discriminative training scheme to train a NTN for each phoneme. The phoneme-based NTN is trained to discriminate the phoneme spoken by the speaker with respect to those spoken by other speakers. A weighted scoring method depending on the phoneme's ability for speaker verification is used to improve the performance. The proposed system is evaluated by experiments on the YOHO database. Performance improvements are obtained over conventional techniques.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114821604","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}
{"title":"Globally-ordered topology-preserving maps achieved with a learning rule performing local weight updates only","authors":"M. V. Van Hulle","doi":"10.1109/NNSP.1995.514883","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514883","url":null,"abstract":"A new unsupervised competitive learning rule is introduced for topology-preserving map formation and vector quantization. The rule, called maximum entropy learning rule (MER), achieves a globally-ordered map by performing local weight updates only. Hence, contrary to Kohonen's self-organizing map algorithm and its many variations, no neighborhood function is needed. The rule yields an equiprobable quantization of a d-dimensional input p.d.f. Simulations are performed to show that the dynamical- and convergence properties of MER are essentially different from those of Kohonen's algorithm.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130671114","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}
{"title":"Simultaneous design of feature extractor and pattern classifier using the minimum classification error training algorithm","authors":"K. Paliwal, M. Bacchiani, Y. Sagisaka","doi":"10.1109/NNSP.1995.514880","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514880","url":null,"abstract":"Recently, a minimum classification error training algorithm has been proposed for minimizing the misclassification probability based on a given set of training samples using a generalized probabilistic descent method. This algorithm is a type of discriminative learning algorithm, but it approaches the objective of minimum classification error in a more direct manner than the conventional discriminative training algorithms. We apply this algorithm for simultaneous design of feature extractor and pattern classifier, and demonstrate some of its properties and advantages.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126355962","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}