Autonomous Learning Systems: From Data Streams to Knowledge by Plamen Angelov

By Plamen Angelov

Autonomous studying Systems is the results of over a decade of concentrated examine and experiences during this rising region which spans a couple of famous and well-established disciplines that come with computer studying, process identity, information mining, fuzzy common sense, neural networks, neuro-fuzzy platforms, keep an eye on idea and development reputation. The evolution of those platforms has been either industry-driven with an expanding call for from sectors reminiscent of defence and defense, aerospace and complicated method industries, bio-medicine and clever transportation, in addition to research-driven – there's a robust development of innovation of the entire above well-established examine disciplines that's associated with their online and real-time program; their adaptability and flexibility.

Providing an advent to the most important applied sciences, precise technical causes of the technique, and an indication of the sensible relevance of the technique with a variety of purposes, this booklet addresses the demanding situations of self sustaining studying platforms with a scientific procedure that lays the rules for a quick starting to be region of study that would underpin various technological functions very important to either and society. 

Key features: 

  • Presents the topic systematically from explaining the basics to illustrating the proposed method with various applications.
  • Covers quite a lot of functions in fields together with unmanned vehicles/robotics, oil refineries, chemical undefined, evolving consumer behaviour and job recognition.
  • Reviews conventional fields together with clustering, category, regulate, fault detection and anomaly detection, filtering and estimation in the course of the prism of evolving and autonomously studying mechanisms.
  • Accompanied by means of an internet site website hosting extra fabric, together with the software program toolbox and lecture notes.

Autonomous studying Systems presents a ‘one-stop store’ at the topic for teachers, scholars, researchers and training engineers. it's also a important reference for presidency organisations and software program developers.

Chapter 1 creation (pages 1–16):
Chapter 2 basics of chance idea (pages 17–36):
Chapter three basics of laptop studying and development reputation (pages 37–59):
Chapter four basics of Fuzzy structures idea (pages 61–81):
Chapter five Evolving approach constitution from Streaming information (pages 83–107):
Chapter 6 self sufficient studying Parameters of the neighborhood Submodels (pages 109–119):
Chapter 7 self reliant Predictors, Estimators, Filters, Inferential Sensors (pages 121–131):
Chapter eight self sustaining studying Classifiers (pages 133–141):
Chapter nine independent studying Controllers (pages 143–153):
Chapter 10 Collaborative self sustaining studying platforms (pages 155–161):
Chapter eleven independent studying Sensors for Chemical and Petrochemical Industries (pages 163–178):
Chapter 12 independent studying platforms in cellular Robotics (pages 179–196):
Chapter thirteen self sufficient Novelty Detection and item monitoring in Video Streams (pages 197–209):
Chapter 14 Modelling Evolving person Behaviour with ALS (pages 211–222):
Chapter 15 Epilogue (pages 223–228):

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Extra info for Autonomous Learning Systems: From Data Streams to Knowledge in Real-time

Example text

It has its roots in statistical analysis (Hastie, Tibshirani and Friedman, 2001) and analysis of the probability density distribution. The rationale is that novelties (respectively, anomalies, outliers) significantly differ and their probability density is significantly lower. Therefore, the test for a data sample to be considered as an outlier/anomalous is to have a low density. The problem of system structure identification, especially in real time, is closely related to the outliers and anomaly detection, because an outlier at a given moment in time may be a start of a new regime of operation or new local model.

A drop of the global density indicates an innovation; an increase of local density indicates a consolidation of a new regime of operation/new local behaviour. 4 Online versus Offline Identification Autonomous systems have to be able to process and extract knowledge from streaming data in a so-called online mode. This means that the data stream is being processed sample-by-sample (here sample also means data item/instant) in a serial fashion, that is, in the same order as the data item was fed to the ALS without having the entire data stream/set available from the start.

28) The proposed expression for the density is nonparametric (it does not have even the pretty generic parameter, h). It does depend on the relative data distribution while the standard probability density distribution assumes independence of the observations, which is true only for handful of problems like tossing coins, throwing die, lottery and other ‘purely’ random phenomena. 1)) usually takes into account only two consecutive observations and cannot take into account all past measurements (this is the reason popular hidden Markov models, HMM are usually considered of so-called first order only).

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