hidden-markov-models

问题 Generic formulation I have a dataset consisting of a sequence of points with 12 features each. I am interested in detecting an event in this data. In the training data I know the moments the event occurred. When the event occurs I can see an observable pattern in the sequence of points before the event. The pattern is formed from about 300 consecutive points. I am interested in detecting when the event occurred in a infinite sequence of points. The analysis happens post factum. I am not

问题 A Simple Markow Chain Let's say we want to estimate parameters of a system such that we can predict the state of the system at timestep t+1 given the state at timestep t. PyMC should be able to deal with this easily. Let our toy system consist of a moving object in a 1D world. The state is the position of the object. We want to estimate the latent variable/the speed of the object. The next state depends on the previous state and the latent variable the speed. # define the system and the data

问题 I am learning about Hidden Markov Models for classifying motion in a sequence of t image frames. Assume I have m dimensions of feature from each frame. Then I cluster it into a symbol (for observable symbol). And I create k different HMM model for k class. Then, how do I determine the number of hidden states for each model to optimise prediction ? Btw, is my approach correct? If I misunderstood how to use it, please correct me:) Thanks :) 回答1: "is my approach already correct?" Your current

问题 I want to ask regarding the meaning of 3-state phone model in HMM. This case is based on the theory of HMM in speech recognition system. So the example is based on the acoustic modeling of the speech sounds in HMM. I get this example picture from a journal paper: http://www.intechopen.com/source/html/41188/media/image8_w.jpg Figure 1: 3-State HMM for the sound /s/ So, my question is: what is it mean by 3 state? what actually S1, S2 & S3 mean? (I know it is state but it represent what?) How to

问题 I need some help implementing a HMM module in R. I'm new to R and don't have a lot of knowledge on it. So i have to implement an IE using HMM, i have 2 folders with files, one with the sentences and the other with the corresponding tags i want to learn form each sentence. folder1 > event1.txt: "2013 2nd International Conference on Information and Knowledge Management (ICIKM 2013) will be held in Chengdu, China during July 20-21, 2013." folder2 > event1.txt: "N: 2nd International Conference on

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 3 years ago . I've been looking into implementations of Hidden Markov Models in C++ lately. I was wondering If I could use any of the existing HMM libraries written in C++ out there to use with Action Recognition (with OpenCV)? I'm tying to AVOID "re-inventing the wheel"! Is it possible to use Torch3Vision even though(looks

问题 I am trying to build a Dynamic Bayesian Network using the BayesServer library in C# for my Unity3D game. I have the following method which implements the network // numberOfDistractors and levelId will be used later for added complexity in modeling void InitializeNetworkForLevel(int numberOfDistractors, int levelId) { beliefNet = new BayesServer.Network(); // add a knowledge node which is a latent variable (parameter to be learned from observed values KTrue = new State("KTrue"); KFalse = new

问题 I am currently working with Scikit Learn and have been running into the following issue while trying to train a Gaussian HMM: File "/Library/Python/2.7/site-packages/sklearn/hmm.py", line 443, in fit self._do_mstep(stats, self.params) File "/Library/Python/2.7/site-packages/sklearn/hmm.py", line 798, in _do_mstep super(GaussianHMM, self)._do_mstep(stats, params) File "/Library/Python/2.7/site-packages/sklearn/hmm.py", line 580, in _do_mstep np.maximum(self.startprob_prior - 1.0 + stats['start

问题 The most alike question I found related to my question is this (simple speech recognition methods) but since had passed 3 years and the answers are not enough I will ask. I want to compute, from scratch, a simple speech recognition system, I only need to recognize five words. As much as I know, the more used audio features for this application are the MFCC, and HMM for classification. I'm able to extract the MFCC from audio but I still have some doubts about how to use the features for

问题 In order to understand cyk algorithm I've worked through example on : https://www.youtube.com/watch?v=VTH1k-xiswM&feature=youtu.be . The result of which is : How do I extract the probabilities associated with each parse and extract the most likely parse tree ? 回答1: These are two distinct problems for PCFG: recognition : does the sentence belong to the language generated by the CFG? (output: yes or no) parsing : what is the highest scoring tree for this sentence? (output: parse tree) The CKY