hidden-markov-models

I am trying to use a hidden Markov model (HMM) for a problem where I have M different observed variables (Yti) and a single hidden variable (Xt) at each time point, t. For clarity, let us assume all observed variables (Yti) are categorical, where each Yti conveys different information and as such may have different cardinalities. An illustrative example is given in the figure below, where M=3. My goal is to train the transition,emission and prior probabilities of an HMM, using the Baum-Welch algorithm, from my observed variable sequences (Yti). Let's say, Xt will initially have 2 hidden states

I am new to sequential learning (& machine learning) & am trying to understand how to use conditional random fields to solve my problem. I have a dataset which is a sequential log of when & where did an end user of my application worked. For example, the following dataset will only have values for User1 User Facility Weekday User1 FacilityA Monday User1 FacilityB Tuesday User1 FacilityC Wednesday ... ... ... I am trying to solve the following problem: Given a weekday and facility a user worked on, what facility & weekday will they work next? To solve this problem, I started looking at

Yes, I'm aware that speech recognition is fairly complicated (as an understatement). What I'm looking for is a method for distinguishing between maybe 20-30 phrases. An ability to split words (discrete speech is fine) would be nice, but isn't required. The software will be user-dependent(i.e. for use by me). I'm not looking for existing software, but for a good way of going about doing this myself. I've looked into various existing methods and it seems like splitting the sound into phonemes, while common, is somewhat excessive for my needs. For some context, I'm just looking for a way to

I'm very new to machine learning, I'v read about Matlab's Statistics toolbox for hidden Markov model, I want to classify a given sequence of signals using it. I'v 3D co-ordinates in matrix P i.e [501x3] and I want to train model based on that. Evert complete trajectory ends on a specfic set of points, i.e at (0,0,0) where it achieves its target. What is the appropriate Pseudocode/approach according to my scenario. My Pseudocode: 501x3 matrix P is Emission matrix where each co-ordinate is state random NxN transition matrix values (but i'm confused in it) generating test sequence using the

I know there is a lot of material related to hidden markov model and I have also read all the questions and answers related to this topic. I understand how it works and how it can be trained, however I am not able to solve the following problem I am having when trying to train it for a simple dynamic gesture. I am using HMM implementation for OpenCV I have looked into previously asked questions and answer here . Which has really helped me in understanding and using markov models. I have total of two dynamic gestures, which are both symmetric (swipe left and swipe right) There are total of 5

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 like) it was designed to work for speech recognition? My idea is that, if we can convert the feature vectors into Symbols/Observations (using Vector Quantization - Kmeans clustering), we can use those symbols for decoding, inference, parameter learning (Baum–Welch algorithm). This way it