scholarly journals A Functional Dynamic Boltzmann Machine

2017 ◽  
Author(s):  
Hiroshi Kajino
Keyword(s):  
Time Series ◽  
Online Learning ◽  
Learning Algorithm ◽  
Interpolation Method ◽  
Time Series Prediction ◽  
Generative Models ◽  
Boltzmann Machine ◽  
Continuous Space ◽  
Functional Time Series ◽  
Consistent Error

Dynamic Boltzmann machines (DyBMs) are recently developed generative models of a time series. They are designed to learn a time series by efficient online learning algorithms, whilst taking long-term dependencies into account with help of eligibility traces, recursively updatable memory units storing descriptive statistics of all the past data. The current DyBMs assume a finite-dimensional time series and cannot be applied to a functional time series, in which the dimension goes to infinity (e.g., spatiotemporal data on a continuous space). In this paper, we present a functional dynamic Boltzmann machine (F-DyBM) as a generative model of a functional time series. A technical challenge is to devise an online learning algorithm with which F-DyBM, consisting of functions and integrals, can learn a functional time series using only finite observations of it. We rise to the above challenge by combining a kernel-based function approximation method along with a statistical interpolation method and finally derive closed-form update rules. We design numerical experiments to empirically confirm the effectiveness of our solutions. The experimental results demonstrate consistent error reductions as compared to baseline methods, from which we conclude the effectiveness of F-DyBM for functional time series prediction.

Online Learning for Time Series Prediction of AR Model with Missing Data

2019 ◽  
Vol 50 (3) ◽  
pp. 2247-2263 ◽  
Author(s):  
Haimin Yang ◽  
Zhisong Pan ◽  
Qing Tao
Keyword(s):  
Time Series ◽  
Online Learning ◽  
Missing Data ◽  
Time Series Prediction ◽  
Ar Model

scholarly journals Experiment Improvement of Restricted Boltzmann Machine Methods for Image Classification

2021 ◽  
pp. 1-16
Author(s):  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Hendry ◽  
Hsiu-Te Hung
Keyword(s):  
Image Classification ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Generative Models ◽  
Restricted Boltzmann Machine ◽  
Model Complexity ◽  
Boltzmann Machine ◽  
Learning Techniques ◽  
Recent Developments ◽  
Hidden Layer

Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of the existing deep networks are based on or related to generative models and image classification. Many applications for RBMs have been developed for a large variety of learning problems. Recent developments have demonstrated the capacity of RBM to be powerful generative models, able to extract useful features from input data or construct deep artificial neural networks. In this work, we propose a learning algorithm to find the optimal model complexity for the RBM by improving the hidden layer (50–750 layers). Then, we compare and analyze the classification performance in depth of regular RBM use RBM () function, classification RBM use stackRBM() function, and Deep Belief Network (DBN) use DBN() function with the different hidden layer. As a result, Stacking RBM and DBN could improve our classification performance compared to regular RBM.

scholarly journals Model-size reduction for reservoir computing by concatenating internal states through time

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yusuke Sakemi ◽  
Kai Morino ◽  
Timothée Leleu ◽  
Kazuyuki Aihara
Keyword(s):  
Time Series ◽  
Learning Algorithm ◽  
Time Series Prediction ◽  
Size Reduction ◽  
Reservoir Computing ◽  
Time Step ◽  
Current Time ◽  
Internal States ◽  
Regression Error ◽  
Model Size

AbstractReservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called “reservoirs.” To implement RC in edge computing, it is highly important to reduce the amount of computational resources that RC requires. In this study, we propose methods that reduce the size of the reservoir by inputting the past or drifting states of the reservoir to the output layer at the current time step. To elucidate the mechanism of model-size reduction, the proposed methods are analyzed based on information processing capacity proposed by Dambre et al. (Sci Rep 2:514, 2012). In addition, we evaluate the effectiveness of the proposed methods on time-series prediction tasks: the generalized Hénon-map and NARMA. On these tasks, we found that the proposed methods were able to reduce the size of the reservoir up to one tenth without a substantial increase in regression error.

scholarly journals ANALYSIS OF SOM & SOFM TECHNIQUES USED IN SATELLITE IMAGERY

2018 ◽  
Vol 4 (2) ◽  
pp. 563-565
Author(s):  
Rachita Sharma ◽  
Sanjay Kumar Dubey
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Time Series ◽  
Satellite Imagery ◽  
Weather Forecasting ◽  
Learning Algorithm ◽  
Time Series Prediction ◽  
Research Area ◽  
Unsupervised Neural Network

This paper describes the introduction of Supervised and Unsupervised Techniques with the comparison of SOFM (Self Organized Feature Map) used for Satellite Imagery. In this we have explained the way of spatial and temporal changes detection used in forecasting in satellite imagery. Forecasting is based on time series of images using Artificial Neural Network. Recently neural networks have gained a lot of interest in time series prediction due to their ability to learn effectively nonlinear dependencies from large volume of possibly noisy data with a learning algorithm. Unsupervised neural networks reveal useful information from the temporal sequence and they reported power in cluster analysis and dimensionality reduction. In unsupervised learning, no pre classification and pre labeling of the input data is needed. SOFM is one of the unsupervised neural network used for time series prediction .In time series prediction the goal is to construct a model that can predict the future of the measured process under interest. There are various approaches to time series prediction that have been used over the years. It is a research area having application in diverse fields like weather forecasting, speech recognition, remote sensing. Advances in remote sensing technology and availability of high resolution images in recent years have motivated many researchers to study patterns in the images for the purpose of trend analysis

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