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IEEE International Workshop on Machine Learning for Signal Processing
IEEE International Workshop on Machine Learning for Signal Processing
召开年:
2016
召开地:
Salerno(IT)
出版时间:
-
会议文集:
-
会议论文
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1.
Multimodal factor analysis
机译:
多峰因子分析
作者:
Yilmaz Yasin
;
Hero Alfred O.
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Gaussian processes;
data analysis;
expectation-maximisation algorithm;
graph theory;
social networking (online);
EM algorithm;
Gaussian observation;
Poisson observation;
Twitter dataset;
expectation-maximization algorithm;
graphical model;
multimodal factor analysis;
multinomial observation;
Clustering algorithms;
Graphical models;
Load modeling;
Loading;
Signal processing algorithms;
Tagging;
Twitter;
Twitter;
graphical models;
multimodal data fusion;
unsupervised learning;
2.
Modeling speech parameter sequences with latent trajectory Hidden Markov model
机译:
建模语音参数序列与潜伏轨迹隐马尔可夫模型
作者:
Kameoka Hirokazu
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
expectation-maximisation algorithm;
hidden Markov models;
probability;
speech processing;
time series;
variational techniques;
vectors;
EM algorithm;
HMM;
data vectors;
decoding;
discrete hidden states;
expectation-maximization algorithm;
latent trajectory hidden Markov model;
optimal state sequence;
piecewise stationary sequences;
probabilistic generative model;
speech parameter sequence modeling;
speech spectra;
time series data;
time-varying sequences;
variational inference algorithm;
vector sequence;
Decoding;
Hidden Markov models;
Inference algorithms;
Joints;
Speech;
Training;
Trajectory;
Expectation-Maximization algorithm;
Hidden Markov model (HMM);
Latent trajectory HMM;
Sequential modeling;
Trajectory HMM;
variational inference;
3.
Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition
机译:
癫痫聚焦本地化使用eEG基于离散小波变换通过全级分解
作者:
Duo Chen
;
Suiren Wan
;
Bao Forrest Sheng
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
discrete wavelet transforms;
electroencephalography;
medical signal processing;
signal classification;
EEG rhythms;
discrete wavelet transform;
electroencephalogram;
epilepsy diagnosis;
epileptic EEG classification;
epileptic focus localization;
full-level decomposition;
signal classification;
Accuracy;
Discrete wavelet transforms;
Electroencephalography;
Epilepsy;
Standards;
DWT;
EEG;
epileptic focus localization;
4.
Discrete independent component analysis (DICA) with belief propagation
机译:
具有信念传播的离散独立分量分析(DICA)
作者:
Palmieri Francesco A. N.
;
Buonanno Amedeo
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Bayes methods;
belief networks;
graph theory;
image coding;
independent component analysis;
inference mechanisms;
learning (artificial intelligence);
Bayesian bipartite graph;
DICA;
MNIST dataset;
belief propagation;
character images;
discrete counterpart-of-independent component analysis;
discrete independent component analysis;
discrete independent hidden variables;
discrete visible variables;
factor graph form;
factorial code;
generative model;
inference;
learning;
Bayes methods;
Belief propagation;
Computer architecture;
Data models;
Encoding;
Independent component analysis;
Training;
Bayesian Networks;
Belief Propagation;
ICA;
5.
Kernel covariance series smoothing
机译:
内核协方差系列平滑
作者:
Soguero-Ruiz Cristina
;
Jenssen Robert
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Gaussian processes;
approximation theory;
covariance matrices;
eigenvalues and eigenfunctions;
smoothing methods;
vectors;
Gaussian process regression;
Nadaraya-Watson kernel smoothing;
covariance function;
covariance matrix;
eigenvalues;
eigenvectors;
kernel covariance series smoothing;
low-rank approximation;
multivariate vector;
sequential random process;
series truncation;
Approximation methods;
Covariance matrices;
Eigenvalues and eigenfunctions;
Estimation;
Kernel;
Noise measurement;
Smoothing methods;
Covariance function;
de-noising;
eigendecomposition;
kernel smoother;
low rank;
orthonormal basis;
regression;
6.
Vowel duration measurement using deep neural networks
机译:
使用深神经网络的元音持续时间测量
作者:
Adi Yossi
;
Keshet Joseph
;
Goldrick Matthew
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
hidden Markov models;
neural nets;
speech processing;
CNN -based forced aligner;
CVC;
DBN;
HMM-based forced aligner;
automatic accurate measurement;
convolutional neural network;
deep belief network;
deep neural network;
deep-network architecture;
labor-intensive manual annotation;
manually annotated data;
phonetic study;
phonetics;
speech segment;
vowel duration measurement;
Context;
Data models;
Hidden Markov models;
Manuals;
Neural networks;
Predictive models;
Speech;
convolution neural networks;
deep belief networks;
forced alignment;
hidden Markov models;
vowel duration measurement;
7.
Piano music transcription with fast convolutional sparse coding
机译:
钢琴音乐转录快速卷积稀疏编码
作者:
Cogliati Andrea
;
Zhiyao Duan
;
Wohlberg Brendt
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Fourier transforms;
audio coding;
music;
time-frequency analysis;
AMT;
MIDI file;
automatic music transcription;
fast convolutional sparse coding;
frequency domain;
median F-measure;
musical signal;
piano keyboard;
piano music transcription;
short time Fourier transform;
symbolic musical representation;
time-domain transcription algorithm;
time-frequency resolution trade-off;
Convolution;
Convolutional codes;
Dictionaries;
Heuristic algorithms;
Time-domain analysis;
Time-frequency analysis;
Automatic Music Transcription;
Convolutional Sparse Coding;
Shift Invariant;
Sparse Representation;
8.
Accelerated graph-based spectral polynomial filters
机译:
加速图基光谱多项式滤波器
作者:
Knyazev Andrew
;
Malyshev Alexander
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
conjugate gradient methods;
eigenvalues and eigenfunctions;
graph theory;
low-pass filters;
matrix algebra;
signal denoising;
spectral analysis;
LOBPCG method;
accelerated graph-based spectral polynomial filters;
bilateral filters;
block locally optimal preconditioned conjugate gradient method;
eigendecomposition;
eigenvalue solvers;
flexible Krylov subspace;
graph Laplacian matrix;
graph-based spectral denoising;
guided filters;
linear solvers;
low-pass filtering;
noisy signal;
polynomial filtering;
Acceleration;
Eigenvalues and eigenfunctions;
Laplace equations;
Noise measurement;
Polynomials;
Symmetric matrices;
Transforms;
Image denoising;
Krylov subspace method;
graph Laplacian;
spectral polynomial filter;
9.
Manifold unwrapping using critical surfaces
机译:
使用临界表面的歧管展开
作者:
Shaker Matineh
;
Kaba M. Devrim
;
Erdogmus Deniz
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
data handling;
gradient methods;
learning (artificial intelligence);
matrix algebra;
polynomial approximation;
probability;
Hessian;
complicated shapes;
curvilinear coordinate systems;
d-dimensional critical surfaces;
data natural skeleton;
determinant expression;
globally unwrapping distributions;
gradient;
local nonlinear coordinate transformations;
locally unwrapping distributions;
low-dimensional underlying structures;
manifold unwrapping;
matrix determinant;
natural high dimensional data distributions;
pdf;
polynomial factorization;
polynomials exponential;
probability density function;
underlying low-dimensional surfaces;
underlying manifold;
zero level set;
Approximation algorithms;
Approximation methods;
MATLAB;
Manifolds;
Polynomials;
Probability density function;
Standards;
10.
Environmental sound classification with convolutional neural networks
机译:
卷积神经网络环境声音分类
作者:
Piczak Karol J.
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
audio signal processing;
cepstral analysis;
neural nets;
signal classification;
audio clip;
audio data;
baseline implementation;
convolutional layer;
convolutional neural network;
environmental recording;
environmental sound classification;
low level representation;
max-pooling;
mel-frequency cepstral coefficient;
public dataset;
segmented spectrogram;
urban recording;
Accuracy;
Convolution;
Convolutional codes;
Neural networks;
Pattern recognition;
Training;
Yttrium;
classification;
convolutional neural networks;
environmental sound;
11.
Universal online prediction via order preserving patterns
机译:
普遍在线预测通过定量保存模式
作者:
Vanli N. Denizcan
;
Sayin Muhammed O.
;
Delibalta Ibrahim
;
Kozat Suleyman S.
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
computational complexity;
equivalence classes;
gradient methods;
prediction theory;
EG algorithm;
FS predictors;
best state assignment;
computational complexity;
exponentiated gradient algorithm;
finite state predictors;
hierarchical equivalence classes;
online compound decision problems;
online learning;
order preserving patterns;
performance measure;
real valued sequences;
sequence history;
sequential prediction;
square loss;
universal online prediction;
Adaptation models;
Computational complexity;
History;
Partitioning algorithms;
Prediction algorithms;
Signal processing algorithms;
Time series analysis;
online learning;
order preserving pattern;
sequential prediction;
12.
Max-margin similarity preserving factor analysis via Gibbs sampling
机译:
通过GIBBS采样保存因子分析的最大边缘相似度
作者:
Buhua Chen
;
Bo Chen
;
Hongwei Liu
;
Xuefeng Zhang
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Markov processes;
Monte Carlo methods;
pattern classification;
support vector machines;
FA;
Gibbs sampling;
LVSVM;
MMSPFA;
SP;
classification criterion;
conditionally conjugate property;
discriminative subspace;
factor analysis model;
latent variable support vector machine;
max-margin constraint;
max-margin similarity preserving factor analysis;
similarity preserving term;
Accuracy;
Analytical models;
Bayes methods;
Data models;
Predictive models;
Support vector machines;
Training;
Factor analysis;
gibbs sampling;
latent variable support vector machine;
max-margin;
similarity preserving term;
13.
Nonparametric Bayesian inference on environmental waters chromatographic profiles
机译:
环境水分色谱型材的非参数贝叶斯推断
作者:
Harant Olivier
;
Foan Louise
;
Bertholon Francois
;
Vignoud Severine
;
Grangeat Pierre
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Bayes methods;
chemical variables measurement;
chromatography;
water pollution;
chromatographic signal;
environmental water chromatographic profile;
lake water;
micropollutant concentration;
molecule retention time;
nonparametric Bayesian inference;
Bayes methods;
Clustering algorithms;
Histograms;
Lakes;
Liquids;
Microscopy;
Shape;
DPMM;
Nonparametric Bayesian Inference;
PAH;
chromatography;
clustering;
micropollutant;
water;
14.
Retrieving sounds by vocal imitation recognition
机译:
通过声乐模仿识别检索声音
作者:
Yichi Zhang
;
Zhiyao Duan
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
acoustic signal processing;
feature extraction;
speech recognition;
support vector machines;
acoustic aspects;
feature extraction;
hand-crafted features;
human communication;
multiclass SVM;
sound retrieval;
stacked auto-encoder;
vocal imitation recognition;
Accuracy;
Feature extraction;
Instruments;
Semantics;
Support vector machines;
Synthesizers;
Sound retrieval;
automatic feature learning;
multi-class classification;
stacked auto-encoder;
vocal imitation;
15.
Preprocessing-free surface material classification using convolutional neural networks pretrained by sparse Autoencoder
机译:
使用稀疏AutoEncoder覆盖的卷积神经网络的预处理表面材料分类
作者:
Mengqi Ji
;
Lu Fang
;
Haitian Zheng
;
Strese Matti
;
Steinbach Eckehard
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
acceleration;
acoustic convolution;
audio coding;
convergence;
haptic interfaces;
learning (artificial intelligence);
neural nets;
signal classification;
ACNN;
RAW acceleration data;
acceleration signals;
automatic feature learning;
convergence;
convolution layers;
object surface;
preprocessing-free surface material classification;
pretrained convolutional neural networks;
publicly available haptic texture database;
rigid tool;
sparse AE;
sparse autoencoder;
trained sparse autoencoder weights;
weight initialization;
Acceleration;
Convolution;
Feature extraction;
Haptic interfaces;
Kernel;
Training;
Transforms;
CNN pretraining;
convolutional neural networks;
haptic texture classification;
sparse Autoencoder;
16.
Securing virtual execution environments through machine learning-based intrusion detection
机译:
通过基于机器学习的入侵检测来保护虚拟执行环境
作者:
Azmandian Fatemeh
;
Kaeli David R.
;
Dy Jennifer G.
;
Aslam Javed A.
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
learning (artificial intelligence);
security of data;
virtual machines;
virtualisation;
anomaly detection;
automatic malicious attack detection;
cyber attacks;
machine learning-based intrusion detection;
server availability;
server consolidation;
server reliability;
virtual execution environment security;
virtual machine isolation;
virtualization;
Feature extraction;
Home appliances;
Intrusion detection;
Machine learning algorithms;
Malware;
Servers;
Virtual machining;
Anomaly Detection;
Cyber-Security;
Machine Learning;
Virtualization;
17.
Synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia
机译:
合成结构磁共振图像发生器改善精神分裂症的深度学习预测
作者:
Ulloa Alvaro
;
Plis Sergey
;
Erhardt Erik
;
Calhoun Vince
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
biomedical MRI;
brain;
medical disorders;
data collection;
deep learning approaches;
human brain;
mental disorders;
physiological abnormalities;
schizophrenia patients;
synthetic realistic training data;
synthetic structural magnetic resonance image generator;
Biological neural networks;
Generators;
Machine learning;
Magnetic resonance imaging;
Neuroimaging;
Probability density function;
Training;
18.
Efficient and distributed tracking of evolving state
机译:
高效和分布式跟踪不断发展状态
作者:
Sayin Muhammed O.
;
Vanli N. Denizcan
;
Delibalta Ibrahim
;
Kozat Suleyman S.
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
distributed processing;
distributed tracking;
learning (artificial intelligence);
software agents;
agents;
centralized processing unit;
communication load;
distributed learning;
distributed network;
distributed tracking algorithm;
evolving states;
information aggregation;
linear model;
state vector;
time-windowing approach;
tracking performance;
Distributed databases;
Heuristic algorithms;
Load modeling;
Markov processes;
Signal processing algorithms;
Steady-state;
Yttrium;
Markov chain;
distributed learning;
dynamic state;
random walk model;
tracking algorithm;
19.
Automatic image tagging and recommendation via PARAFAC2
机译:
通过parafac2自动图像标记和推荐
作者:
Pantraki Evangelia
;
Kotropoulos Constantine
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
feature extraction;
image processing;
matrix algebra;
recommender systems;
social networking (online);
Flickr;
Greek popular tourist landmark;
PARAFAC2;
automatic image annotation;
automatic image tagging;
cross-validation experimental protocol;
feature vector extraction;
image recommendation;
image-feature matrix;
image-tag matrix;
image-user matrix;
multitag information;
multiuser information;
parallel factor analysis 2;
recommendation system;
recommendation vector;
singular vector;
social network;
tag vector;
test image sketch;
visual appearance;
Feature extraction;
Measurement;
Semantics;
Sparse matrices;
Tagging;
Tensile stress;
Training;
Automatic Image Tagging;
Image Recommendation;
Multi-label Classification;
PARAFAC2;
20.
Multiview classification of brain data through tensor factorisation
机译:
通过张量分解的多视图脑数据分类
作者:
Spyrou Loukianos
;
Kouchaki Samaneh
;
Sanei Saeid
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
electroencephalography;
medical signal processing;
signal classification;
tensors;
brain data multiview classification;
brain signals;
epileptic patients;
intracranial electroencephalography data;
neural processes;
tensor factorisation;
Accuracy;
Electrodes;
Electroencephalography;
Feature extraction;
Tensile stress;
Time-frequency analysis;
Training;
brain;
epilepsy;
multiview;
tensor factorisation;
21.
Maximizing margin quality and quantity
机译:
最大化保证金质量和数量
作者:
Yuanzhe Bei
;
Pengyu Hong
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
entropy;
generalisation (artificial intelligence);
learning (artificial intelligence);
minimax techniques;
pattern classification;
UCI machine learning datasets;
classifier learning;
gene expression datasets;
generalizability;
iterative learning algorithm;
large-margin learners;
large-margin principle;
machine learning techniques;
margin quality maximization;
margin quantity maximization;
max-min entropy principle;
Classification algorithms;
Cost function;
Entropy;
Gene expression;
Kernel;
Support vector machines;
Training;
large-margin learning;
margin quality;
22.
Variational Bayes learning of graphical models with hidden variables
机译:
具有隐藏变量的图形模型的变形贝叶斯学习
作者:
Hang Yu
;
Dauwels Justin
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Bayes methods;
learning (artificial intelligence);
maximum likelihood estimation;
Bayesian formulation;
hidden variable graphical models;
high-dimensional data;
maximum penalized likelihood method;
regularization selection;
stability selection;
variational Bayes learning;
Bayes methods;
Damping;
Data models;
Graphical models;
Sparse matrices;
Stability criteria;
Gaussian graphical models;
hidden variables;
regularization selection;
variational Bayes;
23.
Ordinal embedding of unweighted kNN graphs via synchronization
机译:
通过同步序列嵌入未加权的KNN图
作者:
Cucuringu Mihai
;
Woodworth Joseph
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
divide and conquer methods;
graph theory;
learning (artificial intelligence);
synchronisation;
LOE algorithm;
arbitrary similarity transformation;
directed k-nearest neighbor graph;
divide-and-conquer paradigm;
geometric embedding problem;
group synchronization;
local ordinal embedding algorithm;
local-to-global algorithm;
low-dimensional Euclidean space;
machine learning community;
ordinal information;
rigid transformation;
scale synchronization step;
sensor network localization;
unweighted kNN graph;
Approximation algorithms;
Multicore processing;
Noise measurement;
Pipelines;
Robustness;
Synchronization;
Three-dimensional displays;
eigenvector synchronization;
graph embeddings;
k-nearest-neighbor graphs;
ordinal constraints;
24.
Identification of hybrid systems using stable spline kernels
机译:
使用稳定的样条核识别混合动力系统
作者:
Pillonetto Gianluigi
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Bayes methods;
Gaussian processes;
Markov processes;
Monte Carlo methods;
continuous systems;
discrete systems;
identification;
optimisation;
splines (mathematics);
stability;
Bayesian interpretation;
Gaussian process;
HSS;
Markov chain Monte Carlo scheme;
hybrid stable spline algorithm;
hybrid system;
identification;
marginal likelihood optimization;
piecewise affine system;
submodel predictor stability;
Bayes methods;
Indexes;
Kernel;
Markov processes;
Optimization;
Random variables;
Splines (mathematics);
25.
Simultaneous instance annotation and clustering in multi-instance multi-label learning
机译:
多实例多标签学习中的同步实例注释和群集
作者:
Pham Anh T.
;
Raich Raviv
;
Fern Xiaoli Z.
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
expectation-maximisation algorithm;
inference mechanisms;
learning (artificial intelligence);
MIML;
bag label set;
bird song;
expectation maximization inference;
image annotation;
instance annotation;
instance clustering;
maximum likelihood;
multiinstance multilabel learning;
synthetic datasets;
Accuracy;
Birds;
Clustering algorithms;
Computational modeling;
Graphical models;
Logistics;
Yttrium;
26.
Statistical embeddings using a multilayer union of subspaces
机译:
使用多层子空间联盟的统计嵌入
作者:
Taylor Robert M.
;
Necioglu Burhan
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
belief networks;
image segmentation;
statistical analysis;
unsupervised learning;
CalTech-101;
UoS model;
arbitrary sensor modalities;
content-based retrieval;
deep architecture;
deep belief network;
face images;
fixed dimensionality reduction;
globally aligned coordinate system;
image modeling;
image patches;
latent space;
local subspace dimension;
locally linear coordination;
low-dimensional statistical embedding;
observation space;
recursive multilayer union-of-subspaces model;
recursive nested signal segments;
representation learning;
structural similarity index;
unsupervised feature learning;
Bayes methods;
Data models;
Face;
Image reconstruction;
Indexes;
Nonhomogeneous media;
Training;
deep learning;
dimensionality reduction;
locally linear coordination;
mixture of factor analyzers;
statistical embedding;
union of subspaces;
27.
Deep independence network analysis of structural brain imaging: A simulation study
机译:
结构脑成像的深度独立网络分析:模拟研究
作者:
Castro Eduardo
;
Hjelm Devon
;
Plis Sergey
;
Dinh Laurent
;
Turner Jessica
;
Calhoun Vince
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
biological tissues;
biomedical MRI;
brain;
independent component analysis;
learning (artificial intelligence);
medical image processing;
minimisation;
network analysis;
neurophysiology;
ICA;
MRI data;
MRI tissue concentration imaging;
NICE matches;
axial slices;
brain networks;
conventional linear mixture;
deep independence network analysis;
deep learning architecture;
mildly nonlinear mixtures;
minimization criterion;
nonlinear independent component analysis;
spatial components;
structural brain imaging;
structural magnetic resonance imaging data;
synthetic 2D imaging;
Brain;
Couplings;
Imaging;
Independent component analysis;
Jacobian matrices;
Machine learning;
Mutual information;
NICE;
Nonlinear ICA;
deep learning;
simulation;
structural MRI;
28.
Unsupervised segmentation of task activated regions in fMRI
机译:
FMRI中任务激活区域的无监督分割
作者:
Roge Rasmus E.
;
Madsen Kristoffer H.
;
Schmidt MikkelN
;
Morup Morten
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Gaussian processes;
biomedical MRI;
brain;
image denoising;
image resolution;
image segmentation;
medical image processing;
mixture models;
neurophysiology;
BOLD response;
brain;
finger tapping fMRI paradigm;
functional magnetic resonance imaging;
multisubject fMRI data;
noise variances;
nonparametric Gaussian mixture model;
resting-state data;
segmented functional maps;
spatial resolution;
statistical parametric mapping;
supervised group SPM analysis;
task induced functional activations;
unsupervised segmentation;
Correlation;
Gaussian mixture model;
Joints;
Signal to noise ratio;
Time series analysis;
Functional connectivity;
Gaussian Mixture Model;
fMRI analysis;
29.
Studying the interaction of a hidden Markov model with a Bayesian spiking neural network
机译:
研究隐马尔可夫模型与贝叶斯尖刺神经网络的互动
作者:
Tavanaei Amirhossein
;
Maida Anthony S.
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
Bayes methods;
expectation-maximisation algorithm;
hidden Markov models;
learning (artificial intelligence);
neural nets;
pattern classification;
Bayesian spiking neural network;
EM algorithm;
HMM;
SNN;
STDP learning rule;
expectation maximization algorithm;
hidden Markov model;
nonadaptive transition probabilities;
sequential data classification;
spike timing dependency learning rule;
Computational modeling;
Hidden Markov models;
Mathematical model;
Neurons;
Speech;
Speech recognition;
Training;
HMM;
STDP;
Sequential data;
classification;
speech recognition;
spiking neural network;
30.
Characterization of diabetic peripheral neuropathy in infrared video sequences using independent component analysis
机译:
用独立分量分析表征红外视频序列中红外视频序列的糖尿病外周神经病变
作者:
Agurto Carla
;
Barriga Simon
;
Burge Mark
;
Soliz Peter
会议名称:
《IEEE International Workshop on Machine Learning for Signal Processing》
|
2015年
关键词:
biomedical optical imaging;
diseases;
image sequences;
independent component analysis;
infrared imaging;
medical image processing;
neurophysiology;
video signal processing;
DPN detection;
DPN patients;
diabetic peripheral neuropathy;
independent component analysis;
infrared imaging device;
infrared video sequences;
Correlation;
Diabetes;
Foot;
Imaging;
Independent component analysis;
Temperature measurement;
Temperature sensors;
Diabetic peripheral neuropathy;
ICA;
Infrared imaging;
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