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EM算法是機器學習領域中常用的一種算法

EM算法是機器學習領域中常用的一種算法，這個文件是EM算法最簡單的一種實現，即在Gaussian mixture model上面的EM。

標簽： EM算法機器學習算法

上傳時間： 2013-12-11

上傳用戶：wxhwjf
The software implements particle filtering and Rao Blackwellised particle filtering for conditionall

The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.

標簽： filtering particle Blackwellised conditionall

上傳時間： 2014-12-05

上傳用戶：410805624
Probability distribution functions. estimation - (dir) Probability distribution estimation. dsam

Probability distribution functions. estimation - (dir) Probability distribution estimation. dsamp - Generates samples from discrete distribution. erfc2 - Normal cumulative distribution function. gmmsamp - Generates sample from Gaussian mixture model. gsamp - Generates sample from Gaussian distribution. cmeans - C-means (or K-means) clustering algorithm. mahalan - Computes Mahalanobis distance. pdfgauss - Computes probability for Gaussian distribution. pdfgmm - Computes probability for Gaussian mixture model. sigmoid - Evaluates sigmoid function.

標簽： distribution Probability estimation functions

上傳時間： 2016-04-28

上傳用戶：13188549192
EM算法（英文）A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaus

EM算法（英文）A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian mixture and Hidden Markov Models

標簽： Application Estimation Algorithm Parameter

上傳時間： 2017-09-27

上傳用戶：dianxin61
LatentSVM論文

The object detector described below has been initially proposed by P.F. Felzenszwalb in [Felzenszwalb2010]. It is based on a Dalal-Triggs detector that uses a single filter on histogram of oriented gradients (HOG) features to represent an object category. This detector uses a sliding window approach, where a filter is applied at all positions and scales of an image. The first innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a “root” filter (analogous to the Dalal-Triggs filter) plus a set of parts filters and associated deformation models. The score of one of star models at a particular position and scale within an image is the score of the root filter at the given location plus the sum over parts of the maximum, over placements of that part, of the part filter score on its location minus a deformation cost easuring the deviation of the part from its ideal location relative to the root. Both root and part filter scores are defined by the dot product between a filter (a set of weights) and a subwindow of a feature pyramid computed from the input image. Another improvement is a representation of the class of models by a mixture of star models. The score of a mixture model at a particular position and scale is the maximum over components, of the score of that component model at the given location.

標簽： 計算機視覺

上傳時間： 2015-03-15

上傳用戶：sb_zhang
Principles+of+digital+communication

Communication has been one of the deepest needs of the human race throughout recorded history. It is essential to forming social unions, to educating the young, and to expressing a myriad of emotions and needs. Good communication is central to a civilized society. The various communication disciplines in engineering have the purpose of providing technological aids to human communication. One could view the smoke signals and drum rolls of primitive societies as being technological aids to communication, but communication technology as we view it today became important with telegraphy, then telephony, then video, then computer communication, and today the amazing mixture of all of these in inexpensive, small portable devices.

標簽： communication Principles digital of

上傳時間： 2020-05-31

上傳用戶：shancjb
Signal Processing for Telecommunications

This paper presents a Hidden Markov Model (HMM)-based speech enhancement method, aiming at reducing non-stationary noise from speech signals. The system is based on the assumption that the speech and the noise are additive and uncorrelated. Cepstral features are used to extract statistical information from both the speech and the noise. A-priori statistical information is collected from long training sequences into ergodic hidden Markov models. Given the ergodic models for the speech and the noise, a compensated speech-noise model is created by means of parallel model combination, using a log-normal approximation. During the compensation, the mean of every mixture in the speech and noise model is stored. The stored means are then used in the enhancement process to create the most likely speech and noise power spectral distributions using the forward algorithm combined with mixture probability. The distributions are used to generate a Wiener filter for every observation. The paper includes a performance evaluation of the speech enhancer for stationary as well as non-stationary noise environment.

標簽： Telecommunications Processing Signal for

上傳時間： 2020-06-01

上傳用戶：shancjb
Audio+Engineering

Sound is simply an airborne version of vibration. The air which carries sound is a mixture of gases. In gases, the molecules contain so much energy that they break free from their neighbors and rush around at high speed. As Figure 1.1(a) shows, the innumerable elastic collisions of these high-speed molecules produce pressure on the walls of any gas container. If left undisturbed in a container at a constant temperature, eventually the pressure throughout would be constant and uniform.

標簽： Engineering Audio

上傳時間： 2020-06-09

上傳用戶：shancjb