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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

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Fronto-Parietal Gamma-Oscillations are a Cause of Performance Variation in Brain-Computer Interfacing

Fronto-Parietal Gamma-Oscillations are a Cause of Performanc...

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5th international IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) conference on Neural Engineering (NER)

作者： Grosse-Wentrup, Moritz Max Planck Inst Biol Cybernet Dept Empir Inference D-72076 Tubingen Germany

ISBN: (纸本)9781424441419

In recent work, we have provided evidence that fronto-parietal gamma-oscillations of the electromagnetic field of the brain modulate the sensorimotor-rhythm. It is unclear, however, what impact this effect may have on explaining and addressing within-subject performance variations of brain-computer interfaces (BCIs). In this paper, we provide evidence that on a group-average classification accuracies in a two-class motor-imagery paradigm differ by up to 22.2% depending on the state of fronto-parietal gamma-power. As such, this effect may have a large impact on the design of future BCI-systems. We further investigate whether adapting classification procedures to the current state of gamma-power improves classification accuracy, and discuss other approaches to exploiting this effect.

关键词： AC motors Accuracy Brain computer interfaces Electroencephalography machine learning Support vector machines Training brain brain-computer interfaces brain-computer interfacing electroencephalography electromagnetic field fronto-parietal gamma-oscillations group-average classification accuracy handicapped aids medical signal processing performance variations sensorimotor rhythm signal classification two-class motor-imagery paradigm

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5th ACM conference on Recommender Systems - Proceedings of the RecSys'2011 ACM: Challenge on Context-Aware Movie Recommendation, CAMRa 2011

5th ACM Conference on Recommender Systems - Proceedings of t...

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5th ACM conference on Recommender Systems, RecSys'2011: 2nd Challenge on Context-Aware Movie Recommendation, CAMRa 2011

ISBN: (纸本)9781450308250

the proceedings contain 10 papers. the topics discussed include: context-aware recommender systems: the next frontier;group recommendation in context;context-aware movie recommendation based on signal processing and machine learning;group-aware prediction with exponential smoothing for collaborative filtering;informative household recommendation with feature-based matrix factorization;SVD-based group recommendation approaches: an experimental study of Moviepilot;temporal rating habits: a valuable tool for rater differentiation;simple time-aware and social-aware user similarity for a KNN-based recommender system;identifying users from their rating patterns;a heuristic approach to identifying the specific household member for a given rating;and mining relational context-aware graph for rater identification.

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A signal denoising method for text meaning vectors

A signal denoising method for text meaning vectors

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Asia Modelling Symposium 2011: 5th Asia international conference on Mathematical Modelling and Computer Simulation, AMS 2011

作者： Hernández, Sergio Sallis, Philip Garden, Kathy Lab. de Proc. de Inf. Geoespacial Universidad Católica Del Maule Talca Chile Geoinformatics Research Centre Auckland University of Technology Auckland New Zealand Information Technology Research Ltd Auckland New Zealand

ISBN: (纸本)9780769544144

the extraction of meaning or at least inter-dependencies using data and text mining methods is well understood. Numerous approaches have been taken to select relevant information from often very large data sets. the discarding of items that are not relevant to a parameterized retrieval is usually based on an 'include or do not include' decision imbedded in some kind of branch-and-bound algorithm, made to a varying extent sophisticated by the use of machine learning techniques. this paper addresses the discarding process as noise elimination within the context of well-established signal processing methods. It proposes an entropy-based approach using a value-weighted matrix for word relevance matching, where whole text is partitioned according to whether there is a direct relevance of word pairs to the declared meaning being sought, which is expressed as a set of parameters and the noise is considered as errors in the data stream. the resulting non-noisy data is depicted as a text meaning vector, where terms of direct relevance to the initial parameter values are stored. © 2011 IEEE.

关键词： Sentiment analysis

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KPCA vs. PCA study for an age classification of speakers

KPCA vs. PCA study for an age classification of speakers

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5th international conference on Nonlinear Speech processing, NOLISP 2011

作者： Muñoz-Mulas, Cristina Martínez-Olalla, Rafael Gómez-Vilda, Pedro Lang, Elmar W. Álvarez-Marquina, Agustín Mazaira-Fernández, Luis Miguel Nieto-Lluis, Víctor Departamento de Arquitectura de Tecnologías Y Sistemas Informáticos Universidad Politécnica de Madrid Campus de Montegancedo s/n 28660 Boadilla del Monte Madrid Spain Computational Intelligence and Machine Learning Group Institut of Biophysik Universität of Regensburg Universitätsstr. 31 Regensburg D-93040 Germany

ISBN: (纸本)9783642250194

Kernel-PCA and PCA techniques are compared in the task of age and gender separation. A feature extraction process that discriminates between vocal tract and glottal source is implemented. the reason why speech is processed in that way is because vocal tract length and resonant characteristics are related to gender and age and there is also a great relationship between glottal source and age and gender. the obtained features are then processed with PCA and kernel-PCA techniques. the results show that gender and age separation is possible and that kernel-PCA (especially with RBF kernel) clearly outperforms classical PCA or no preprocessing features. © 2011 Springer-Verlag.

关键词： Artificial intelligence

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Semi-supervised learning of acoustic driven prosodic phrase breaks for text-to-speech systems 5

Semi-supervised learning of acoustic driven prosodic phrase ...

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5th international conference on Speech Prosody: Every Language, Every Style, SP 2010

作者： Prahallad, Kishore Veera Raghavendra, E. Black, Alan W. International Institute of Information Technology Hyderabad India Language Technologies Institute Carnegie Mellon University United States

In this paper, we propose a semi-supervised learning of acoustic driven phrase breaks and its usefulness for text-to-speech systems. In this work, we derive a set of initial hypothesis of phrase breaks in a speech signal using pause as an acoustic cue. As these initial estimates are obtained based on knowledge of speech production and speech signal processing, one could treat the hypothesized phrase break regions as labeled data. Features such as duration, F0 and energy are extracted from these labeled regions and a machine learning model is trained to perform the classification of these acoustic features as belonging to the class of a phrase break or not a phrase break. We then attempt to bootstrap the machine learning model using unlabeled data (i.e., the rest of the data). © 2010 Proceedings of the international conference on Speech Prosody.

关键词： Speech synthesis

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FIXED-BUDGET KERNEL RECURSIVE LEAST-SQUARES

FIXED-BUDGET KERNEL RECURSIVE LEAST-SQUARES

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IEEE international conference on Acoustics, Speech, and signal processing

作者： Steven Van Vaerenbergh Ignacio Santamaria Weifeng Liu Jose C. Principe Dept. of Communications Engineering University of Cantabria Santander Spain Forecasting Team *** 701 5th Ave Seattle WA 98104 USA Dept. of Electrical and Computer Engineering University of Florida Gainesville FL 32611 USA

ISBN: (纸本)9781424442959

We present a kernel-based recursive least-squares (KRLS) algorithm on a fixed memory budget, capable of recursively learning a nonlinear mapping and tracking changes over time. In order to deal with the growing support inherent to online kernel methods, the proposed method uses a combined strategy of growing and pruning the support. In contrast to a previous sliding-window based technique, the presented algorithm does not prune the oldest data point in every time instant but it instead aims to prune the least significant data point. We also introduce a label update procedure to equip the algorithm with tracking capability. Simulations show that the proposed method obtains better performance than state-of-the-art kernel adaptive filtering techniques given similar memory requirements.

关键词： Kernel methods machine learning recursive least-squares nonlinear filtering fixed budget Nonlinear filters kernel method Seeds recursive least squares Artificial Intelligence machine learning Prune memory requirements Read Only Memory Device Component

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Drilling mud signal processing based on wavelet

Drilling mud signal processing based on wavelet

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5th international conference on Wavelet Analysis and Pattern Recognition

作者： Li, Chuan-Wei Mu, De-Jun Li, An-Zong Liao, Qi-Mei Qu, Jing-Hui Northwestern Polytech Univ Coll Automatizat Xian 710072 Shaanxi Peoples R China China Petr Logging Co Xian 710061 Shaanxi Peoples R China Fourth Mil Med Univ Fac Biomed Engn Xian 710032 Shaanxi Peoples R China

ISBN: (纸本)9781424410651

Wavelet transform has been used widely in signal processing and the wavelet base and other parameters are very important for the effect of signal processing and the extracting of the useful signal. the signals of Measurement While Drilling (MWD) usually contained pressure pulsation mid noises because of the influence of the measurement condition in the oil field. the correlation coefficient presented in this article may serve as an effective way for selection of wavelet base and scale parameters. the wavelet transform with suitable wavelet base and parameters selected by means of the method extracted the useful signals free from noises, which satisfied the requirement of MWD. therefore, wavelet transform could process effectively the drilling mud signals to recover the raw encoded data. the correlation coefficient obtained by us was applicable to the evaluation of the similarities of the original and processed signals.

关键词： measurement while drilling mud signal wavelet transform correlation coefficient de-noising

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Protein fold recognition using residue-based alignments of sequence and secondary structure

Protein fold recognition using residue-based alignments of s...

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32nd IEEE international conference on Acoustics, Speech and signal processing

作者： Aydin, Zafer Erdogan, Hakan Altunbasak, Yucel Georgia Inst Technol Ctr Signal & Image Proc Centergy 5th Floor Atlanta GA 30332 USA Sabanchi Univ Fac Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9781424407286

Protein structure prediction aims to determine the three-dimensional structure of proteins form their amino acid sequences. When a protein does not have similarity (homology) to any known fold, threading or fold recognition methods are used to predict structure. Fold recognition methods frequently employ secondary structure, solvent accessibility, and evolutionary information to enhance the accuracy and the quality of the predictions. In this paper, we present a residue based alignment method as an alternative to the state-of-the-art SSEA method, originally introduced by Przytycka et al. [1], and further modified by McGuffin et al. [2]. We introduce a residue-based score function, which can incorporate amino acid similarity matrices such as BLOSUM into secondary structure similarity scoring and compute joint alignments. We show that the power of the SSEA method comes from the length normalization instead of the element alignment technique and similar performance can be achieved using residue-based alignments of secondary structures by optimizing gap costs. In simulations with the two benchmark datasets, our method performs slightly better than the SSEA in terms of the fold recognition accuracy. When the secondary structure similarity matrix is combined with the amino acid based BLOSUM30 matrix, the accuracy of our method improves further (4% for the McGuffin set and 10% for the Ding and Dubchak set). the availability of aligning the amino acid and secondary structure sequences in a joint manner offers a better starting point for more elaborate techniques that employ profile-profile alignments and machine learning methods [3,4].

关键词： protein fold recognition secondary structure alignment amino acid alignment score normalization gap cost

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Ridgelet transform with application in ground penetrating radar processing

Ridgelet transform with application in ground penetrating ra...

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5th international conference on Wavelet Analysis and Pattern Recognition

作者： Zhang, Zhi-Yu Zhang, Jiu-Long Yu, Hai-Yan Lu, Yan-Jun Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Xian Inst Post & Telecomm Dept Informat Xian 710121 Peoples R China

ISBN: (纸本)9781424410651

Ridgelet is the new development of wavelet dealing with line singularities in signals. In ground penetrating radar (GPR) signal, the direct wave is a strong noise which has line singularity. In lime-space domain, the useful informnation in GPR signal is badly polluted by strong direct wave so that the underground targets can not be recognized correctly Ridgelet overcomes the disadvantages of wavelets in dealing g with high dimensions that if can effectively represent singularities along lines or hyperplanes. Concerning the characteristics of GPR signals, the ridgelet transform is employed to remove the background noise and separate the direct wave. the simulation results for real GPR signals demonstrate its effectiveness.

关键词： ridgelet transform Ground Penetrating Radar(GPR) background noise clutter reduction

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Research on wavelet estimation via cumulant-based ARMA model approach

Research on wavelet estimation via cumulant-based ARMA model...

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5th international conference on Wavelet Analysis and Pattern Recognition

作者： Dai, Yong-Shou Wang, Jun-Ling Wang, Wei-Wei Wei, Lei China Univ Petr Dong Ying 257061 Peoples R China

ISBN: (纸本)9781424410651

On the assumption that the seismic wavelet is non-causal and mixed 'phase, ARMA(autoregressive moving, average) model was utilized to describe the seismic wavelet, and a cumulant-based parametric estimation approach which sythesized both the linear (matrix equation)and nonlinear(cumulant matching) methods was proposed to estimate the wavelet parameters. In this approach, the cumulant matching g approach is used for accurate parameter estimation, with the basis of the initial guess that generaled from matrix equations. theoretic analysis and numerical simulation demonstrate the feasibility of this approach. Compared with the potential computational error of the linear methods, this approach can improve parameter estimation precision. Moreover;it extracts wavelet with high computational efficiency by avoiding the use of cumulant matching method under MA (moving, average) model description, and reduces the complexity of initial guess via ARMA model matching approach.

关键词： high-order cumulant ARMA (autoregressive moving average) linear and nonlinear combination signal estimation wavelet extraction seismological signal processing

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