Abstract
The problem of recognizing patterns, when there are few training data available, is particularly relevant and arises in cases when collection of training data is expensive or essentially impossible. The work proposes a new probability model MC&CL (Markov Chain and Clusters) based on a combination of markov chain and algorithm of clustering (self-organizing map of Kohonen, k-means method), to solve a problem of classifying sequences of observations, when the amount of training dataset is low. An original experimental comparison is made between the developed model (MC&CL) and a number of the other popular models to classify sequences: HMM (Hidden Markov Model), HCRF (Hidden Conditional Random Fields),LSTM (Long Short-Term Memory), kNN+DTW (k-Nearest Neighbors algorithm + Dynamic Time Warping algorithm). A comparison is made using synthetic random sequences, generated from the hidden markov model, with noise added to training specimens. The best accuracy of classifying the suggested model is shown, as compared to those under review, when the amount of training data is low.
Highlights
Sequences of observations are classified in the process of solving the problems of recognizing: speech [1, 2], hand-written text [3], gestures of hands/head [4, 5], states of technical objects [6, 7, 8] Due to intense introduction of computer-aided learning into various areas of human activities, machine learning engineers often have to deal with small-scale training sets, which structure and characteristics are almost unknown
MC&CL (Markov Chain and CLusters) method implies development and modification of probability model, we have previously developed, which is based on markov chain and self-organizing map of Kohonen/Growing neural gas [21, 22]
Sequences of observations for training and test datasets shall be generated from hidden markov model with random parameters of distribution
Summary
Sequences of observations are classified in the process of solving the problems of recognizing: speech [1, 2], hand-written text [3], gestures of hands/head [4, 5], states of technical objects [6, 7, 8] Due to intense introduction of computer-aided learning into various areas of human activities, machine learning engineers often have to deal with small-scale training sets, which structure and characteristics are almost unknown. To classify the sequences of observations, the following machine learning methods have widely been used: Hidden Markov Model (HMM), Hidden Conditional Random Fields (HCRF), Long Short-Term Memory (LSTM), k-Nearest Neighbors algorithm (kNN) with Dynamic Time Warping algorithm (DTW). KNN method is a popular metric non-parametric algorithm of classification It is based on computing a distance between test specimen and specimens from the training set. Several studies on applying kNN-method together with DTW algorithm were undertaken by Professor Eamonn Keogh and his colleagues.
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