£112.39

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£112 today · previous high £112 · all-time low £108

Springer Learning in Non-Stationary Environments - AI Methods

Amazon

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£112.39 £107.54 £108.60 £109.66 £110.71 £111.77 £112.83 28 July 2026 19 August 2026 10 September 2026 02 October 2026 24 October 2026
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54 days 16 days 19 days · current 0 14 27 41 54 £108 £110 £112 Days at Price

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Most common price: £108 (54 days, 60.7%)

Price range: £108 - £112

Price levels: 3 different prices over 89 days

Description

This comprehensive guide from Springer explores cutting-edge methods for building adaptive systems that learn and predict in unpredictable, non-stationary environments. It addresses core challenges in machine learning including modelling, prediction, classification, and data processing when conditions constantly change. The book presents practical approaches and recent techniques essential for developing intelligent systems that can adjust to dynamic real-world conditions. Perfect for AI researchers, machine learning engineers, and data scientists seeking to understand how to design robust solutions that maintain accuracy despite environmental shifts. Whether you're working on streaming data applications, online learning systems, or adaptive algorithms, this resource provides the theoretical foundations and applied methods needed to tackle non-stationary problem spaces effectively.

Key Features

Springer-published resource covering methods for systems that learn and adapt in changing environments.

Addresses modelling, prediction, and classification challenges specific to non-stationary data scenarios.

Presents recent approaches for data understanding and processing in unpredictable conditions.

Practical guide for designing adaptive machine learning systems that handle environmental shifts.

Comprehensive coverage of methods to maintain system performance despite dynamic, unpredictable changes.

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
08 May 2014
Listed Since
09 May 2014

Barcode

No barcode data available

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