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Electronic Nose: Algorithmic Challenges

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Cover of 'Electronic Nose: Algorithmic Challenges'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 E-Nose Algorithms and Challenges
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    Chapter 3 Heuristic and Bio-inspired Neural Network Model
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    Chapter 4 Chaos-Based Neural Network Optimization Approach
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    Chapter 5 Multilayer Perceptron-Based Concentration Estimation
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    Chapter 6 Discriminative Support Vector Machine-Based Odor Classification
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    Chapter 7 Local Kernel Discriminant Analysis-Based Odor Recognition
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    Chapter 8 Ensemble of Classifiers for Robust Recognition
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    Chapter 9 Chaotic Time Series-Based Sensor Drift Prediction
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    Chapter 10 Domain Adaptation Guided Drift Compensation
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    Chapter 11 Domain Regularized Subspace Projection Method
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    Chapter 12 Cross-Domain Subspace Learning Approach
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    Chapter 13 Domain Correction-Based Adaptive Extreme Learning Machine
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    Chapter 14 Multi-feature Semi-supervised Learning Approach
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    Chapter 15 Pattern Recognition-Based Interference Reduction
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    Chapter 16 Pattern Mismatch Guided Interference Elimination
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    Chapter 17 Self-expression-Based Abnormal Odor Detection
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    Chapter 18 Affine Calibration Transfer Model
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    Chapter 19 Instrumental Batch Correction
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    Chapter 20 Book Review and Future Work
Attention for Chapter 14: Multi-feature Semi-supervised Learning Approach
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