A probabilistic neural network is applied as a tool to approximate the statistical evaluation function for a simple version of the game Tic-Tac-Toe. We solve the problem by a sequential estimation of the underlying di...
详细信息
A probabilistic neural network is applied as a tool to approximate the statistical evaluation function for a simple version of the game Tic-Tac-Toe. We solve the problem by a sequential estimation of the underlying discrete distribution mixture of product components. (c) 2005 Elsevier B.V. All rights reserved.
Recently, two extensions of neural gas have been proposed: a fast batch version of neural gas for data given in advance, and extensions of neural gas to learn a (possibly fuzzy) supervised classification. Here we prop...
详细信息
ISBN:
(纸本)3540379517
Recently, two extensions of neural gas have been proposed: a fast batch version of neural gas for data given in advance, and extensions of neural gas to learn a (possibly fuzzy) supervised classification. Here we propose a batch version for supervised neural gas training which allows to efficiently learn a prototype-based classification, provided training data are given beforehand. The method relies on a simpler cost function than online supervised neural gas and leads to simpler update formulas. We prove convergence of the algorithm in a general framework, which also incorporates supervised k-means and supervised batch-SOM, and which opens the way towards metric adaptation as well as application to proximity data not embedded in a real-vector space.
This paper addresses the problem of animal detection in natural environment from aerial videos. Since the natural environment is usually composed of several fundamental elements such as trees, grass, streams, etc., it...
详细信息
ISBN:
(纸本)9783319461823;9783319461816
This paper addresses the problem of animal detection in natural environment from aerial videos. Since the natural environment is usually composed of several fundamental elements such as trees, grass, streams, etc., it is proposed to distinguish the animal by categorizing the background into several classes. From the manually labeled samples, texture as well as brightness features are extracted to train a feedforwardneural Network. Then the classifier is applied to filter the test frame to locate potential animal regions. Four texture measures calculated from Grey Level Co-occurrence Matrix (GLCM) are used for texture feature description. Instead of obtaining these texture measures from grey level images, it is proposed to carry out calculation for every channel of the RGB image. The implemented results illustrate that this feature extraction method works well and the texture feature is a decisive factor in background categorizing.
This article offers an open vocabulary Arabic text recognition system using two neuralnetworks, one for segmentation and another one for characters recognition. The problem of words segmentation in Arabic language, l...
详细信息
ISBN:
(纸本)9783319461823;9783319461816
This article offers an open vocabulary Arabic text recognition system using two neuralnetworks, one for segmentation and another one for characters recognition. The problem of words segmentation in Arabic language, like many cursive languages, presents a challenge to the OCR systems. This paper presents a multichannel neural network to solve offline segmentation of machine-printed Arabic documents. The segmented characters are then used as input to a convolutional neural network for Arabic characters recognition. The accuracy of the segmentation model using one font is 98.9%, while four-font model showed 95.5% accuracy. The accuracy of characters recognition on Arabic Transparent font of size 18 pt from APTI data set is 94.8%.
We extend the self-organizing map (SOM) in the form as proposed by Heskes to a supervised fuzzy classification method. On the one hand, this leads to a robust classifier where efficient learning with fuzzy labeled or ...
详细信息
ISBN:
(纸本)3540379517
We extend the self-organizing map (SOM) in the form as proposed by Heskes to a supervised fuzzy classification method. On the one hand, this leads to a robust classifier where efficient learning with fuzzy labeled or partially contradictory data is possible. On the other hand, the integration of labeling into the location of prototypes in a SOM leads to a visualization of those parts of the data relevant for the classification.
We present here a complete system for the localization of facial features in frontal face images. In the first step, face detection is performed using Viola & Jones state of art algorithm. Then, a cascade of neura...
详细信息
ISBN:
(纸本)9783642121586
We present here a complete system for the localization of facial features in frontal face images. In the first step, face detection is performed using Viola & Jones state of art algorithm. Then, a cascade of neuralnetworks localizes precisely 28 facial features. The first network performs a coarse detection of three areas in the image corresponding roughly to left arid right eyes and mouths. Then, three local networks localize, in these areas, 9 key points per eye and 10 key points on the mouth. Thorough experiments on 3500 images from standard databases (Feret, BioID) show the detector accuracy, its generalization ability and speed.
Deep Learning Library (DLL) is a library for machine learning with deep neuralnetworks that focuses on speed. It supports feedforwardneuralnetworks such as fully-connected artificialneuralnetworks (ANNs) and Conv...
详细信息
ISBN:
(纸本)9783319999784;9783319999777
Deep Learning Library (DLL) is a library for machine learning with deep neuralnetworks that focuses on speed. It supports feedforwardneuralnetworks such as fully-connected artificialneuralnetworks (ANNs) and Convolutional neuralnetworks (CNNs). Our main motivation for this work was to propose and evaluate novel software engineering strategies with potential to accelerate runtime for training and inference`. Such strategies are mostly independent of the underlying deep learning algorithms. On three different datasets and for four different neural network models, we compared DLL to five popular deep learning libraries. Experimentally, it is shown that the proposed library is systematically and significantly faster on CPU and GPU. In terms of classification performance, similar accuracies as the other libraries are reported.
Keyword spotting refers to the process of retrieving all instances of a given key word in a document. in the present paper, a novel keyword spotting system for handwritten documents is described. It is derived from a ...
详细信息
ISBN:
(纸本)9783642121586
Keyword spotting refers to the process of retrieving all instances of a given key word in a document. in the present paper, a novel keyword spotting system for handwritten documents is described. It is derived from a neural network based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e. it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm. We demonstrate that such a system has the potential for high performance. For example, a precision of 95% at 50% recall is reached for the 41,000 most frequent words on the IAM offline handwriting database.
The automatic extraction of the notes that were played in a digital musical signal (automatic music transcription) is an open problem. A number of techniques have been applied to solve it without concluding results. T...
详细信息
The automatic extraction of the notes that were played in a digital musical signal (automatic music transcription) is an open problem. A number of techniques have been applied to solve it without concluding results. The monotimbral polyphonic version of the problem is posed here: a single instrument has been played and more than one note can sound at the same time. This work tries to approach it through the identification of the pattern of a given instrument in the frequency domain. This is achieved using time-delay neuralnetworks that are fed with the band-grouped spectrogram of a polyphonic monotimbral music recording. The use of a learning scheme based on examples like neuralnetworks permits our system to avoid the use of an auditory model to approach this problem. A number of issues have to be faced to have a robust and powerful system, but promising results using synthesized instruments are presented. (c) 2005 Elsevier B.V. All rights reserved.
In this work we present extensions for Radial Basis Function networks to improve their ability for discrete and continuous pain intensity estimation. Besides proposing a mid-level fusion scheme, the use of standardiza...
详细信息
ISBN:
(纸本)9783319461823;9783319461816
In this work we present extensions for Radial Basis Function networks to improve their ability for discrete and continuous pain intensity estimation. Besides proposing a mid-level fusion scheme, the use of standardization and unconventional loss functions are covered. We show that RBF networks can be improved in this way and present extensive experimental validation to support our findings on a multi-modal dataset.
暂无评论