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Learning with Limited Supervision by Input and Output CodingZhang, Yi 01 May 2012 (has links)
In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high. Even with a relatively large number of labeled examples, the learning problem may still suffer from limited supervision as the complexity of the prediction function increases. Therefore, learning with limited supervision presents a major challenge to machine learning. With the goal of supervision reduction, this thesis studies the representation, discovery and incorporation of extra input and output information in learning.
Information about the input space can be encoded by regularization. We first design a semi-supervised learning method for text classification that encodes the correlation of words inferred from seemingly irrelevant unlabeled text. We then propose a multi-task learning framework with a matrix-normal penalty, which compactly encodes the covariance structure of the joint input space of multiple tasks. To capture structure information that is more general than covariance and correlation, we study a class of regularization penalties on model compressibility. Then we design the projection penalty, which encodes the structure information from a dimension reduction while controlling the risk of information loss.
Information about the output space can be exploited by error correcting output codes. Using the composite likelihood view, we propose an improved pairwise coding for multi-label classification, which encodes pairwise label density (as opposed to label comparisons) and decodes using variational methods. We then investigate problemdependent codes, where the encoding is learned from data instead of being predefined. We first propose a multi-label output code using canonical correlation analysis, where predictability of the code is optimized. We then argue that both discriminability and predictability are critical for output coding, and propose a max-margin formulation that promotes both discriminative and predictable codes.
We empirically study our methods in a wide spectrum of applications, including document categorization, landmine detection, face recognition, brain signal classification, handwritten digit recognition, house price forecasting, music emotion prediction, medical decision, email analysis, gene function classification, outdoor scene recognition, and so forth. In all these applications, our proposed methods for encoding input and output information lead to significantly improved prediction performance.
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Classificação automática de modulações mono e multiportadoras utilizando método de extração de características e classificadores SVMAmoedo, Diego Alves, 69-98468-0910 19 July 2017 (has links)
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Previous issue date: 2017-07-19 / Cognitive radio is a new technology that aims to solve the spectrumunderutilization
problem, through spectrum sensing, whose objective is to detect
the so called spectrum holes. Automatic modulation classi cation plays an important
role in this scenario, since it provides information about primary users, with
the goal of aiding in spectrum sensing tasks. In the present dissertation, we propose
a methodology for multiclass and hierarchical classi cation of modulated signal
using support vector machines (SVM), with a set of prede ned parameters. In literature,
other works deal with automatic modulation classi cation with SVM and
other classi ers, however, few of them take a deep look at classi er design. SVM is
known by its high discrimantion capacity, but its performance is very sensitive to
the parameters used during classi ers design. With the use of a prede ned set of parameters,
we seek to analyze the behavior of the classi er broadly and to investigate
the in uence of parameter changes on the constitution of classi ers. In addition,
we use one-versus-all and one-versus-one, error-correcting output codes and hierarchical
decomposition. Finally, nine types of modulations (AM, FM, BPSK, QPSK, 16QAM, 64QAM, GMSK, OFDM and WCDMA) are used. The types of modulation
as well as the decomposition techniques used cover almost all decomposition
techniques and modulation classes present in the literature. / O Rádio Cognitivo é uma nova tecnologia que busca resolver o problema de
subutilização do espectro de radiofrequências, por meio do sensoriamento do espectro,
cujo objetivo é detectar os buracos espectrais. A classi cação automática de
modulação desempenha um papel importante neste cenário, pois, provém informa-
ção sobre os usuários primários de modo a auxiliar nas tarefas de sensoriamento do
espectro. Nesta dissertação, propomos uma metodologia para a classi cação multiclasse
e hierárquica de sinais modulados utilizando SVM, com um conjunto de
parâmetros pré-de nidos. Na literatura, outros trabalhos tratam da classi cação
automática de modulação tanto com SVM como com outros tipos de classi cadores,
porém, poucos fazem uma análise detalhada do projeto dos classi cadores. O SVM
é conhecido por sua alta capacidade de discriminação, todavia, seu desempenho
é bastante sensível aos parâmetros usados na geração dos classi cadores. Com a
utilização de um conjunto pré-de nido de parâmetros, buscamos analisar o comportamento
do classi cador de forma ampla e investigar a in uência das mudanças de
parâmetros na constituição de classi cadores. Além disso, utiliza-se as técnicas de
decomposição multiclasse um-contra-todos, um-contra-um, códigos de saída corretores
de erros e hierárquica. Por m, foram utilizados nove tipos de modulações (AM,
FM, BPSK, QPSK, 16QAM, 64QAM, GMSK, OFDM e WCDMA). Tanto os tipos de modulação quanto as técnicas de decomposição abrangem quase a totalidade de
técnicas de decomposição e de classes de modulação presentes na literatura.
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Rozpoznání hudebního slohu z orchestrální nahrávky za pomoci technik Music Information Retrieval / Recognition of music style from orchestral recording using Music Information Retrieval techniquesJelínková, Jana January 2020 (has links)
As all genres of popular music, classical music consists of many different subgenres. The aim of this work is to recognize those subgenres from orchestral recordings. It is focused on the time period from the very end of 16th century to the beginning of 20th century, which means that Baroque era, Classical era and Romantic era are researched. The Music Information Retrieval (MIR) method was used to classify chosen subgenres. In the first phase of MIR method, parameters were extracted from musical recordings and were evaluated. Only the best parameters were used as input data for machine learning classifiers, to be specific: kNN (K-Nearest Neighbor), LDA (Linear Discriminant Analysis), GMM (Gaussian Mixture Models) and SVM (Support Vector Machines). In the final chapter, all the best results are summarized. According to the results, there is significant difference between the Baroque era and the other researched eras. This significant difference led to better identification of the Baroque era recordings. On the contrary, Classical era ended up to be relatively similar to Romantic era and therefore all classifiers had less success in identification of recordings from this era. The results are in line with music theory and characteristics of chosen musical eras.
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