Gotowe bibliografie tematyczne / Minimum Classification Error algorithm

Gotowa bibliografia na temat „Minimum Classification Error algorithm”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 20 lutego 2023

Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych

Wybierz rodzaj źródła:

Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Minimum Classification Error algorithm”.

Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.

Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.

Artykuły w czasopismach na temat "Minimum Classification Error algorithm"

CHEN, LIANG-HUA, SHAO-HUA DENG i HONG-YUAN LIAO. "MCE-BASED FACE RECOGNITION". International Journal of Pattern Recognition and Artificial Intelligence 15, nr 08 (grudzień 2001): 1311–27. http://dx.doi.org/10.1142/s0218001401001477.

Pełny tekst źródła

Streszczenie:

This paper proposes a complete procedure for the extraction and recognition of human faces in complex scenes. The morphology-based face detection algorithm can locate multiple faces oriented in any direction. The recognition algorithm is based on the minimum classification error (MCE) criterion. In our work, the minimum classification error formulation is incorporated into a multilayer perceptron neural network. Experimental results show that our system is robust to noisy images and complex background.

Style APA, Harvard, Vancouver, ISO itp.

Xu, Yong, Xiaozhao Fang, Qi Zhu, Yan Chen, Jane You i Hong Liu. "Modified minimum squared error algorithm for robust classification and face recognition experiments". Neurocomputing 135 (lipiec 2014): 253–61. http://dx.doi.org/10.1016/j.neucom.2013.11.025.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Toh, Kar-Ann. "Deterministic Neural Classification". Neural Computation 20, nr 6 (czerwiec 2008): 1565–95. http://dx.doi.org/10.1162/neco.2007.04-07-508.

Pełny tekst źródła

Streszczenie:

This letter presents a minimum classification error learning formulation for a single-layer feedforward network (SLFN). By approximating the nonlinear counting step function using a quadratic function, the classification error rate is shown to be deterministically solvable. Essentially the derived solution is related to an existing weighted least-squares method with class-specific weights set according to the size of data set. By considering the class-specific weights as adjustable parameters, the learning formulation extends the classification robustness of the SLFN without sacrificing its intrinsic advantage of being a closed-form algorithm. While the method is applicable to other linear formulations, our empirical results indicate SLFN's effectiveness on classification generalization.

Style APA, Harvard, Vancouver, ISO itp.

Fan, Yu. "Study on Cooperative Multipoint Communication Precoding Algorithm under SLNR-MMSE Framework". Advances in Multimedia 2022 (31.07.2022): 1–10. http://dx.doi.org/10.1155/2022/9457248.

Pełny tekst źródła

Streszczenie:

With the rapid growth of demand for wireless data services and the continuous introduction of new air interface technologies, mobile communication systems continue to face new challenges in supporting high-speed multimedia service transmission and achieving seamless coverage. In order to meet the requirements of the IMS system in terms of bandwidth, peak rate, communication throughput, etc., multiantenna enhancement technology and cooperative multipoint transmission technology have become research hotspots as key technologies. In the study of multiuser system, this paper focuses on the precoding technology based on noncode book, based on the minimum mean square error criterion and the maximum letter leakage noise ratio criterion, studies the precoding technology of different multiuser systems, expounds the collaborative multipoint transmission system, and makes a basic classification. The signal leakage-to-noise ratio precoding algorithm and the minimum mean square error precoding algorithm are analyzed in detail. In view of the shortcomings of these two algorithms, this paper takes the minimum sum of the total mean square error of the system as the optimization goal of the combinations of precoding and power allocation. The precoding algorithm of SLNR-MMSE is proposed. The simulation analysis shows that the proposed algorithm has certain advantages over other algorithms in terms of bit error rate and system capacity. It shows that this study is important for optimizing collaborative multipoint communication system.

Style APA, Harvard, Vancouver, ISO itp.

Huang, Wei, Xiaohui Wang, Yuzhen Jiang i Yinghui Zhu. "Two-Directional Minimum Squared Error Algorithm and Classification Experiments on Face and Building Images". Journal of Computational and Theoretical Nanoscience 12, nr 11 (1.11.2015): 4654–60. http://dx.doi.org/10.1166/jctn.2015.4414.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Liu, Zhonghua, Shan Xue, Lin Zhang, Jiexin Pu i Haijun Wang. "An Improved Kernel Minimum Square Error Classification Algorithm Based on $L_{2,1}$ -Norm Regularization". IEEE Access 5 (2017): 14133–40. http://dx.doi.org/10.1109/access.2017.2730218.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Hu, Zheng-ping, Yan Peng i Shuhuan Zhao. "A new sparse representation algorithm based on kernel spatial non-minimum residual error for classification". Optik 126, nr 23 (grudzień 2015): 4665–70. http://dx.doi.org/10.1016/j.ijleo.2015.08.088.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

ZHANG, RUI, XIAO QING DING i HAI LONG LIU. "DISCRIMINATIVE TRAINING BASED QUADRATIC CLASSIFIER FOR HANDWRITTEN CHARACTER RECOGNITION". International Journal of Pattern Recognition and Artificial Intelligence 21, nr 06 (wrzesień 2007): 1035–46. http://dx.doi.org/10.1142/s0218001407005776.

Pełny tekst źródła

Streszczenie:

In offline handwritten character recognition, the classifier with modified quadratic discriminant function (MQDF) has achieved good performance. The parameters of MQDF classifier are commonly estimated by the maximum likelihood (ML) estimator, which maximizes the within-class likelihood instead of directly minimizing the classification errors. To improve the performance of MQDF classifier, in this paper, the MQDF parameters are revised by discriminative training using a minimum classification error (MCE) criterion. The proposed algorithm is applied to recognizing handwritten numerals and handwritten Chinese characters, the recognition rates obtained are among the highest that have ever been reported.

Style APA, Harvard, Vancouver, ISO itp.

Zhang, Mingmei, Yongan Xue, Yonghui Ge i Jinling Zhao. "Watershed Segmentation Algorithm Based on Luv Color Space Region Merging for Extracting Slope Hazard Boundaries". ISPRS International Journal of Geo-Information 9, nr 4 (17.04.2020): 246. http://dx.doi.org/10.3390/ijgi9040246.

Pełny tekst źródła

Streszczenie:

To accurately identify slope hazards based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm is proposed. The color difference of the Luv color space was used as the regional similarity measure for region merging. Furthermore, the area relative error for evaluating the image segmentation accuracy was improved and supplemented with the pixel quantity error to evaluate the segmentation accuracy. An unstable slope was identified to validate the algorithm on Chinese Gaofen-2 (GF-2) remote sensing imagery by a multiscale segmentation extraction experiment. The results show the following: (1) the optimal segmentation and merging scale parameters were, respectively, minimum threshold constant C for minimum area Amin of 500 and optimal threshold D for a color difference of 400. (2) The total processing time for segmentation and merging of unstable slopes was 39.702 s, much lower than the maximum likelihood classification method and a little more than the object-oriented classification method. The relative error of the slope hazard area was 4.92% and the pixel quantity error was 1.60%, which were superior to the two classification methods. (3) The evaluation criteria of segmentation accuracy were consistent with the results of visual interpretation and the confusion matrix, indicating that the criteria established in this study are reliable. By comparing the time efficiency, visual effect and classification accuracies, the proposed method has a good comprehensive extraction effect. It can provide a technical reference for promoting the rapid extraction of slope hazards based on remote sensing imagery. Meanwhile, it also provides a theoretical and practical experience reference for improving the watershed segmentation algorithm.

Style APA, Harvard, Vancouver, ISO itp.

Koloko, R. J. Koloko, P. Ele, R. Wamkeue i A. Melingui. "Fault Detection and Classification of a Photovoltaic Generator Using the BES Optimization Algorithm Associated with SVM". International Journal of Photoenergy 2022 (8.11.2022): 1–14. http://dx.doi.org/10.1155/2022/6841861.

Pełny tekst źródła

Streszczenie:

In this work, an innovative approach based on the estimation of the photovoltaic generator (GPV) parameters from the Bald Eagle Search (BES) optimization algorithm, associated with a support vector machine (SVM) classification algorithm, allowed to highlight a new tool for the classification of the signatures of shading and moisture PV defects. It recognizes signatures generated by the GPV in healthy and erroneous operation using the optimized parametric vector and classifies defects using the same optimized vector. The technique emphasizes the resilience of parameter estimate in terms of error on all parameters. The classification accuracy is 93%. The residuals between the estimated curve in healthy operation with a minimum error of the order of 10-4 and the one at fault are used as an indicator of faults.

Style APA, Harvard, Vancouver, ISO itp.

Więcej źródeł

Rozprawy doktorskie na temat "Minimum Classification Error algorithm"

Wang, Xuechuan, i n/a. "Feature Extraction and Dimensionality Reduction in Pattern Recognition and Their Application in Speech Recognition". Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030619.162803.

Pełny tekst źródła

Streszczenie:

Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are the two popular independent feature extraction algorithms. Both of them extract features by projecting the parameter vectors into a new feature space through a linear transformation matrix. But they optimize the transformation matrix with different intentions. PCA optimizes the transformation matrix by finding the largest variations in the original feature space. LDA pursues the largest ratio of between-class variation and within-class variation when projecting the original feature space to a subspace. The drawback of independent feature extraction algorithms is that their optimization criteria are different from the classifiers minimum classification error criterion, which may cause inconsistency between feature extraction and the classification stages of a pattern recognizer and consequently, degrade the performance of classifiers. A direct way to overcome this problem is to conduct feature extraction and classification jointly with a consistent criterion. Minimum classification Error (MCE) training algorithm provides such an integrated framework. MCE algorithm was first proposed for optimizing classifiers. It is a type of discriminative learning algorithm but achieves minimum classification error directly. The flexibility of the framework of MCE algorithm makes it convenient to conduct feature extraction and classification jointly. Conventional feature extraction and pattern classification algorithms, LDA, PCA, MCE training algorithm, minimum distance classifier, likelihood classifier and Bayesian classifier, are linear algorithms. The advantage of linear algorithms is their simplicity and ability to reduce feature dimensionalities. However, they have the limitation that the decision boundaries generated are linear and have little computational flexibility. SVM is a recently developed integrated pattern classification algorithm with non-linear formulation. It is based on the idea that the classification that a.ords dot-products can be computed efficiently in higher dimensional feature spaces. The classes which are not linearly separable in the original parametric space can be linearly separated in the higher dimensional feature space. Because of this, SVM has the advantage that it can handle the classes with complex nonlinear decision boundaries. However, SVM is a highly integrated and closed pattern classification system. It is very difficult to adopt feature extraction into SVMs framework. Thus SVM is unable to conduct feature extraction tasks. This thesis investigates LDA and PCA for feature extraction and dimensionality reduction and proposes the application of MCE training algorithms for joint feature extraction and classification tasks. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithms in joint feature and classification tasks. SVM, as a non-linear pattern classification system is also investigated in this thesis. A reduced-dimensional SVM (RDSVM) is proposed to enable SVM to conduct feature extraction and classification jointly. All of the investigated and proposed algorithms are tested and compared firstly on a number of small databases, such as Deterding Vowels Database, Fishers IRIS database and Germans GLASS database. Then they are tested in a large-scale speech recognition experiment based on TIMIT database.

Style APA, Harvard, Vancouver, ISO itp.

Wang, Xuechuan. "Feature Extraction and Dimensionality Reduction in Pattern Recognition and Their Application in Speech Recognition". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/365680.

Pełny tekst źródła

Streszczenie:

Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are the two popular independent feature extraction algorithms. Both of them extract features by projecting the parameter vectors into a new feature space through a linear transformation matrix. But they optimize the transformation matrix with different intentions. PCA optimizes the transformation matrix by finding the largest variations in the original feature space. LDA pursues the largest ratio of between-class variation and within-class variation when projecting the original feature space to a subspace. The drawback of independent feature extraction algorithms is that their optimization criteria are different from the classifier’s minimum classification error criterion, which may cause inconsistency between feature extraction and the classification stages of a pattern recognizer and consequently, degrade the performance of classifiers. A direct way to overcome this problem is to conduct feature extraction and classification jointly with a consistent criterion. Minimum classification Error (MCE) training algorithm provides such an integrated framework. MCE algorithm was first proposed for optimizing classifiers. It is a type of discriminative learning algorithm but achieves minimum classification error directly. The flexibility of the framework of MCE algorithm makes it convenient to conduct feature extraction and classification jointly. Conventional feature extraction and pattern classification algorithms, LDA, PCA, MCE training algorithm, minimum distance classifier, likelihood classifier and Bayesian classifier, are linear algorithms. The advantage of linear algorithms is their simplicity and ability to reduce feature dimensionalities. However, they have the limitation that the decision boundaries generated are linear and have little computational flexibility. SVM is a recently developed integrated pattern classification algorithm with non-linear formulation. It is based on the idea that the classification that a.ords dot-products can be computed efficiently in higher dimensional feature spaces. The classes which are not linearly separable in the original parametric space can be linearly separated in the higher dimensional feature space. Because of this, SVM has the advantage that it can handle the classes with complex nonlinear decision boundaries. However, SVM is a highly integrated and closed pattern classification system. It is very difficult to adopt feature extraction into SVM’s framework. Thus SVM is unable to conduct feature extraction tasks. This thesis investigates LDA and PCA for feature extraction and dimensionality reduction and proposes the application of MCE training algorithms for joint feature extraction and classification tasks. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithms in joint feature and classification tasks. SVM, as a non-linear pattern classification system is also investigated in this thesis. A reduced-dimensional SVM (RDSVM) is proposed to enable SVM to conduct feature extraction and classification jointly. All of the investigated and proposed algorithms are tested and compared firstly on a number of small databases, such as Deterding Vowels Database, Fisher’s IRIS database and German’s GLASS database. Then they are tested in a large-scale speech recognition experiment based on TIMIT database.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Microelectronic Engineering
Full Text

Style APA, Harvard, Vancouver, ISO itp.

Han, Seungju. "A family of minimum Renyi's error entropy algorithm for information processing". [Gainesville, Fla.] : University of Florida, 2007. http://purl.fcla.edu/fcla/etd/UFE0021428.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Fu, Qiang. "A generalization of the minimum classification error (MCE) training method for speech recognition and detection". Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22705.

Pełny tekst źródła

Streszczenie:

The model training algorithm is a critical component in the statistical pattern recognition approaches which are based on the Bayes decision theory. Conventional applications of the Bayes decision theory usually assume uniform error cost and result in a ubiquitous use of the maximum a posteriori (MAP) decision policy and the paradigm of distribution estimation as practice in the design of a statistical pattern recognition system. The minimum classification error (MCE) training method is proposed to overcome some substantial limitations for the conventional distribution estimation methods. In this thesis, three aspects of the MCE method are generalized. First, an optimal classifier/recognizer design framework is constructed, aiming at minimizing non-uniform error cost.A generalized training criterion named weighted MCE is proposed for pattern and speech recognition tasks with non-uniform error cost. Second, the MCE method for speech recognition tasks requires appropriate management of multiple recognition hypotheses for each data segment. A modified version of the MCE method with a new approach to selecting and organizing recognition hypotheses is proposed for continuous phoneme recognition. Third, the minimum verification error (MVE) method for detection-based automatic speech recognition (ASR) is studied. The MVE method can be viewed as a special version of the MCE method which aims at minimizing detection/verification errors. We present many experiments on pattern recognition and speech recognition tasks to justify the effectiveness of our generalizations.

Style APA, Harvard, Vancouver, ISO itp.

Albarakati, Noor. "FAST NEURAL NETWORK ALGORITHM FOR SOLVING CLASSIFICATION TASKS". VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2740.

Pełny tekst źródła

Streszczenie:

Classification is one-out-of several applications in the neural network (NN) world. Multilayer perceptron (MLP) is the common neural network architecture which is used for classification tasks. It is famous for its error back propagation (EBP) algorithm, which opened the new way for solving classification problems given a set of empirical data. In the thesis, we performed experiments by using three different NN structures in order to find the best MLP neural network structure for performing the nonlinear classification of multiclass data sets. A developed learning algorithm used here is the batch EBP algorithm which uses all the data as a single batch while updating the NN weights. The batch EBP speeds up training significantly and this is also why the title of the thesis is dubbed 'fast NN …'. In the batch EBP, and when in the output layer a linear neurons are used, one implements the pseudo-inverse algorithm to calculate the output layer weights. In this way one always finds the local minimum of a cost function for a given hidden layer weights. Three different MLP neural network structures have been investigated while solving classification problems having K classes: one model/K output layer neurons, K separate models/One output layer neuron, and K joint models/One output layer neuron. The extensive series of experiments performed within the thesis proved that the best structure for solving multiclass classification problems is a K joint models/One output layer neuron structure.

Style APA, Harvard, Vancouver, ISO itp.

Chen, Nan. "An IEEE 802.15.4 Packet Error Classification Algorithm : Discriminating Between Multipath Fading and Attenuation and WLAN". Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-24918.

Pełny tekst źródła

Streszczenie:

In wireless sensor networks, communications are usually destroyed by signal attenuation, multipath fading and different kinds of interferences like WLAN and microwave oven interference. In order to build a stable wireless communication system, reactions like retransmission mechanisms are necessary. Since the way we must react to interference is different from the way we react to multipathfading and attenuation, the retransmission mechanism should be adjusted in different ways under those different cicumstances. Under this condition, channel diagnostics for discriminating the causes that corrupt the packets between multipath fading and attenuation (MFA) and WLAN interference are imperative. This paper presents a frame bit error rate (F-BER) regulated algorithm based on a joint RSSI-LQI classifier that may correctly diagnose the channel status. This discriminator is implemented on MicaZ sensor devices equipped with CC2420 transceivers. This discriminator is able to improve the accuracy to 91%. Although we need to wait for 2 or 3 necessary packets to make a decision, higher stability and reliability are presented when operating this discriminator.

Style APA, Harvard, Vancouver, ISO itp.

Kunert, Gerd. "Anisotropic mesh construction and error estimation in the finite element method". Universitätsbibliothek Chemnitz, 2000. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200000033.

Pełny tekst źródła

Streszczenie:

In an anisotropic adaptive finite element algorithm one usually needs an error estimator that yields the error size but also the stretching directions and stretching ratios of the elements of a (quasi) optimal anisotropic mesh. However the last two ingredients can not be extracted from any of the known anisotropic a posteriori error estimators. Therefore a heuristic approach is pursued here, namely, the desired information is provided by the so-called Hessian strategy. This strategy produces favourable anisotropic meshes which result in a small discretization error. The focus of this paper is on error estimation on anisotropic meshes. It is known that such error estimation is reliable and efficient only if the anisotropic mesh is aligned with the anisotropic solution. The main result here is that the Hessian strategy produces anisotropic meshes that show the required alignment with the anisotropic solution. The corresponding inequalities are proven, and the underlying heuristic assumptions are given in a stringent yet general form. Hence the analysis provides further inside into a particular aspect of anisotropic error estimation.

Style APA, Harvard, Vancouver, ISO itp.

Shin, Sung-Hwan. "Objective-driven discriminative training and adaptation based on an MCE criterion for speech recognition and detection". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50255.

Pełny tekst źródła

Streszczenie:

Acoustic modeling in state-of-the-art speech recognition systems is commonly based on discriminative criteria. Different from the paradigm of the conventional distribution estimation such as maximum a posteriori (MAP) and maximum likelihood (ML), the most popular discriminative criteria such as MCE and MPE aim at direct minimization of the empirical error rate. As recent ASR applications become diverse, it has been increasingly recognized that realistic applications often require a model that can be optimized for a task-specific goal or a particular scenario beyond the general purposes of the current discriminative criteria. These specific requirements cannot be directly handled by the current discriminative criteria since the objective of the criteria is to minimize the overall empirical error rate. In this thesis, we propose novel objective-driven discriminative training and adaptation frameworks, which are generalized from the minimum classification error (MCE) criterion, for various tasks and scenarios of speech recognition and detection. The proposed frameworks are constructed to formulate new discriminative criteria which satisfy various requirements of the recent ASR applications. In this thesis, each objective required by an application or a developer is directly embedded into the learning criterion. Then, the objective-driven discriminative criterion is used to optimize an acoustic model in order to achieve the required objective. Three task-specific requirements that the recent ASR applications often require in practice are mainly taken into account in developing the objective-driven discriminative criteria. First, an issue of individual error minimization of speech recognition is addressed and we propose a direct minimization algorithm for each error type of speech recognition. Second, a rapid adaptation scenario is embedded into formulating discriminative linear transforms under the MCE criterion. A regularized MCE criterion is proposed to efficiently improve the generalization capability of the MCE estimate in a rapid adaptation scenario. Finally, the particular operating scenario that requires a system model optimized at a given specific operating point is discussed over the conventional receiver operating characteristic (ROC) optimization. A constrained discriminative training algorithm which can directly optimize a system model for any particular operating need is proposed. For each of the developed algorithms, we provide an analytical solution and an appropriate optimization procedure.

Style APA, Harvard, Vancouver, ISO itp.

Du, Zekun. "Algorithm Design and Optimization of Convolutional Neural Networks Implemented on FPGAs". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254575.

Pełny tekst źródła

Streszczenie:

Deep learning develops rapidly in recent years. It has been applied to many fields, which are the main areas of artificial intelligence. The combination of deep learning and embedded systems is a good direction in the technical field. This project is going to design a deep learning neural network algorithm that can be implemented on hardware, for example, FPGA. This project based on current researches about deep learning neural network and hardware features. The system uses PyTorch and CUDA as assistant methods. This project focuses on image classification based on a convolutional neural network (CNN). Many good CNN models can be studied, like ResNet, ResNeXt, and MobileNet. By applying these models to the design, an algorithm is decided with the model of MobileNet. Models are selected in some ways, like floating point operations (FLOPs), number of parameters and classification accuracy. Finally, the algorithm based on MobileNet is selected with a top-1 error of 5.5%on software with a 6-class data set.Furthermore, the hardware simulation comes on the MobileNet based algorithm. The parameters are transformed from floating point numbers to 8-bit integers. The output numbers of each individual layer are cut to fixed-bit integers to fit the hardware restriction. A number handling method is designed to simulate the number change on hardware. Based on this simulation method, the top-1 error increases to 12.3%, which is acceptable.
Deep learning har utvecklats snabbt under den senaste tiden. Det har funnit applikationer inom många områden, som är huvudfälten inom Artificial Intelligence. Kombinationen av Deep Learning och innbyggda system är en god inriktning i det tekniska fältet. Syftet med detta projekt är att designa en Deep Learning-baserad Neural Network algoritm som kan implementeras på hårdvara, till exempel en FPGA. Projektet är baserat på modern forskning inom Deep Learning Neural Networks samt hårdvaruegenskaper.Systemet är baserat på PyTorch och CUDA. Projektets fokus är bild klassificering baserat på Convolutional Neural Networks (CNN). Det finns många bra CNN modeller att studera, t.ex. ResNet, ResNeXt och MobileNet. Genom att applicera dessa modeller till designen valdes en algoritm med MobileNetmodellen. Valet av modell är baserat på faktorer så som antal flyttalsoperationer, antal modellparametrar och klassifikationsprecision. Den mjukvarubaserade versionen av den MobileNet-baserade algoritmen har top-1 error på 5.5En hårdvarusimulering av MobileNet nätverket designades, i vilket parametrarna är konverterade från flyttal till 8-bit heltal. Talen från varje lager klipps till fixed-bit heltal för att anpassa nätverket till befintliga hårdvarubegränsningar. En metod designas för att simulera talförändringen på hårdvaran. Baserat på denna simuleringsmetod reduceras top-1 error till 12.3

Style APA, Harvard, Vancouver, ISO itp.

Palkki, Ryan D. "Chemical identification under a poisson model for Raman spectroscopy". Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/45935.

Pełny tekst źródła

Streszczenie:

Raman spectroscopy provides a powerful means of chemical identification in a variety of fields, partly because of its non-contact nature and the speed at which measurements can be taken. The development of powerful, inexpensive lasers and sensitive charge-coupled device (CCD) detectors has led to widespread use of commercial and scientific Raman systems. However, relatively little work has been done developing physics-based probabilistic models for Raman measurement systems and crafting inference algorithms within the framework of statistical estimation and detection theory. The objective of this thesis is to develop algorithms and performance bounds for the identification of chemicals from their Raman spectra. First, a Poisson measurement model based on the physics of a dispersive Raman device is presented. The problem is then expressed as one of deterministic parameter estimation, and several methods are analyzed for computing the maximum-likelihood (ML) estimates of the mixing coefficients under our data model. The performance of these algorithms is compared against the Cramer-Rao lower bound (CRLB). Next, the Raman detection problem is formulated as one of multiple hypothesis detection (MHD), and an approximation to the optimal decision rule is presented. The resulting approximations are related to the minimum description length (MDL) approach to inference. In our simulations, this method is seen to outperform two common general detection approaches, the spectral unmixing approach and the generalized likelihood ratio test (GLRT). The MHD framework is applied naturally to both the detection of individual target chemicals and to the detection of chemicals from a given class. The common, yet vexing, scenario is then considered in which chemicals are present that are not in the known reference library. A novel variation of nonnegative matrix factorization (NMF) is developed to address this problem. Our simulations indicate that this algorithm gives better estimation performance than the standard two-stage NMF approach and the fully supervised approach when there are chemicals present that are not in the library. Finally, estimation algorithms are developed that take into account errors that may be present in the reference library. In particular, an algorithm is presented for ML estimation under a Poisson errors-in-variables (EIV) model. It is shown that this same basic approach can also be applied to the nonnegative total least squares (NNTLS) problem. Most of the techniques developed in this thesis are applicable to other problems in which an object is to be identified by comparing some measurement of it to a library of known constituent signatures.

Style APA, Harvard, Vancouver, ISO itp.

Więcej źródeł

Książki na temat "Minimum Classification Error algorithm"

J. P. Marques de Sá. Minimum Error Entropy Classification. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

Znajdź pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Marques de Sá, Joaquim P., Luís M. A. Silva, Jorge M. F. Santos i Luís A. Alexandre. Minimum Error Entropy Classification. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29029-9.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Joaquim P. Marques de Sá, Luís A. Alexandre, Luís M. A. Silva i Jorge M. F. Santos. Minimum Error Entropy Classification. Springer, 2014.

Znajdź pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Minimum Error Entropy Classification Studies in Computational Intelligence. Springer, 2012.

Znajdź pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Części książek na temat "Minimum Classification Error algorithm"

Wen, Bo, Ganlin Shan i Xiusheng Duan. "Research of Incremental Learning Algorithm Based on the Minimum Classification Error Criterion". W Lecture Notes in Electrical Engineering, 637–43. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4790-9_83.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Marques de Sá, Joaquim P., Luís M. A. Silva, Jorge M. F. Santos i Luís A. Alexandre. "Introduction". W Minimum Error Entropy Classification, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29029-9_1.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Marques de Sá, Joaquim P., Luís M. A. Silva, Jorge M. F. Santos i Luís A. Alexandre. "Continuous Risk Functionals". W Minimum Error Entropy Classification, 13–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29029-9_2.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Marques de Sá, Joaquim P., Luís M. A. Silva, Jorge M. F. Santos i Luís A. Alexandre. "MEE with Continuous Errors". W Minimum Error Entropy Classification, 41–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29029-9_3.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Marques de Sá, Joaquim P., Luís M. A. Silva, Jorge M. F. Santos i Luís A. Alexandre. "MEE with Discrete Errors". W Minimum Error Entropy Classification, 93–120. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29029-9_4.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Marques de Sá, Joaquim P., Luís M. A. Silva, Jorge M. F. Santos i Luís A. Alexandre. "EE-Inspired Risks". W Minimum Error Entropy Classification, 121–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29029-9_5.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Marques de Sá, Joaquim P., Luís M. A. Silva, Jorge M. F. Santos i Luís A. Alexandre. "Applications". W Minimum Error Entropy Classification, 139–213. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29029-9_6.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Ma, Boning, Longxing Kong, Xiaoan Tang i Gangyao Kuang. "The Reverse Loop Subdivision Algorithm on Approximate Minimum Error". W Lecture Notes in Electrical Engineering, 811–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38466-0_90.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Lim, Chee Peng, i Robert F. Harrison. "Minimal Error Rate Classification in a Non-stationary Environment via a Modified Fuzzy ARTMAP Network". W Artificial Neural Nets and Genetic Algorithms, 503–6. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_130.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Shimodaira, Hiroshi, Jun Rokui i Mitsuru Nakai. "Modified minimum classification error learning and its application to neural networks". W Advances in Pattern Recognition, 785–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033303.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Streszczenia konferencji na temat "Minimum Classification Error algorithm"

Ota, Kensuke, Shigeru Katagiri i Miho Ohsaki. "Minimum Classification Error training employing Real-Coded Genetic Algorithms". W TENCON 2012 - 2012 IEEE Region 10 Conference. IEEE, 2012. http://dx.doi.org/10.1109/tencon.2012.6412201.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Zhao, Yunxin, Ping Chen, Paul D. Gader i Yue Zhang. "Combined evolutionary algorithm and minimum classification error training for DHMM-based land mine detection". W AeroSense 2002, redaktorzy J. Thomas Broach, Russell S. Harmon i Gerald J. Dobeck. SPIE, 2002. http://dx.doi.org/10.1117/12.479077.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Paliwal, Kuldip K., M. Bacchiani i Yoshinori Sagisaka. "Minimum classification error training algorithm for feature extractor and pattern classifier in speech recognition". W 4th European Conference on Speech Communication and Technology (Eurospeech 1995). ISCA: ISCA, 1995. http://dx.doi.org/10.21437/eurospeech.1995-30.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Zhang, Lijun, Hichem Frigui i Paul Gader. "Context-Dependent Fusion of Multiple Algorithms with Minimum Classification Error Learning". W 2009 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2009. http://dx.doi.org/10.1109/icmla.2009.119.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Temizel, Cenk, Celal Hakan Canbaz, Yildiray Palabiyik, Hakki Aydin, Minh Tran, Mustafa Hakan Ozyurtkan, Mesut Yurukcu i Paul Johnson. "A Thorough Review of Machine Learning Applications in Oil and Gas Industry". W SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205720-ms.

Pełny tekst źródła

Streszczenie:

Abstract Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one.

Style APA, Harvard, Vancouver, ISO itp.

Watanabe, Hideyuki, Shigeru Katagiri, Kouta Yamada, Erik McDermott, Atsushi Nakamura, Shinji Watanabe i Miho Ohsaki. "Minimum Error Classification with geometric margin control". W 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495645.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Cai, Peng, Dongyuan Lin, Wenxing Yan i Shiyuan Wang. "Diffusion Recursive Minimum Error Entropy Algorithm". W ICDSP 2022: 2022 6th International Conference on Digital Signal Processing. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3529570.3529606.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Brandes, Tim, Stefano Scarso, Christian Koch i Stephan Staudacher. "Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups". W ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-58673.

Pełny tekst źródła

Streszczenie:

Abstract A numerical experiment of intentionally reduced complexity is used to demonstrate a method to classify flight missions in terms of the operational severity experienced by the engines. In this proof of concept, the general term of severity is limited to the erosion of the core flow compressor blade and vane leading edges. A Monte Carlo simulation of varying operational conditions generates a required database of 10000 flight missions. Each flight is sampled at a rate of 1 Hz. Eleven measurable or synthesizable physical parameters are deemed to be relevant for the problem. They are reduced to seven universal non-dimensional groups which are averaged for each flight. The application of principal component analysis allows a further reduction to three principal components. They are used to run a support-vector machine model in order to classify the flights. A linear kernel function is chosen for the support-vector machine due to its low computation time compared to other functions. The robustness of the classification approach against measurement precision error is evaluated. In addition, a minimum number of flights required for training and a sensible number of severity classes are documented. Furthermore, the importance to train the algorithms on a sufficiently wide range of operations is presented.

Style APA, Harvard, Vancouver, ISO itp.

Chang, Yang-Lang, Jyh-Perng Fang, Wen-Yew Liang, Lena Chang i Kun-Shan Chen. "Multisource Image Classification Based on Parallel Minimum Classification Error Learning". W IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779379.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Fangzhi Zhu, Rui Yan i Yong Sun. "Improved minimum squared error method for robust classification". W 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2014. http://dx.doi.org/10.1109/ccis.2014.7175705.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

Raporty organizacyjne na temat "Minimum Classification Error algorithm"

Searcy, Stephen W., i Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, sierpień 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

Pełny tekst źródła

Streszczenie:

This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.

Style APA, Harvard, Vancouver, ISO itp.

Oferujemy zniżki na wszystkie plany premium dla autorów, których prace zostały uwzględnione w tematycznych zestawieniach literatury. Skontaktuj się z nami, aby uzyskać unikalny kod promocyjny!