Research on the Concept of Liquid State Machine

10/08/2019
by   Gideon Gbenga Oladipupo, et al.
30

Liquid State Machine (LSM) is a neural model with real time computations which transforms the time varying inputs stream to a higher dimensional space. The concept of LSM is a novel field of research in biological inspired computation with most research effort on training the model as well as finding the optimum learning method. In this review, the performance of LSM model was investigated using two learning method, online learning and offline (batch) learning methods. The review revealed that optimal performance of LSM was recorded through online method as computational space and other complexities associated with batch learning is eliminated.

READ FULL TEXT

page 1

page 3

page 5

research
09/21/2020

Selectivity correction with online machine learning

Computer systems are full of heuristic rules which drive the decisions t...
research
03/15/2021

TinyOL: TinyML with Online-Learning on Microcontrollers

Tiny machine learning (TinyML) is a fast-growing research area committed...
research
02/15/2023

Quantum Learning Theory Beyond Batch Binary Classification

Arunachalam and de Wolf (2018) showed that the sample complexity of quan...
research
10/20/2020

Real-Time Optimisation for Online Learning in Auctions

In display advertising, a small group of sellers and bidders face each o...
research
07/13/2012

Learning the Pseudoinverse Solution to Network Weights

The last decade has seen the parallel emergence in computational neurosc...
research
10/24/2021

An efficient estimation of time-varying parameters of dynamic models by combining offline batch optimization and online data assimilation

It is crucially important to estimate unknown parameters in earth system...
research
07/15/2021

Online Learning for Recommendations at Grubhub

We propose a method to easily modify existing offline Recommender System...

Please sign up or login with your details

Forgot password? Click here to reset