| | Leaning model | Machine learning | Deep learning |
| | Application scenarios | (i) small signal data | (i) high-dimensional signal data | | (ii) signal under relatively ideal conditions | (ii) good feasibility in real field environment |
| | Algorithms | (i) ANN [26, 37]
(ii) KNN [38, 91]
(iii) SVM [6, 27, 47, 48, 92]
(iv) Naïve Bayes [39]
(v) HMM [46]
(vi) Fuzzy classifier [93]
(vii) Polynomial classifier [40, 94] | (i) DNN [24, 30, 31, 61]
(ii) DBN [49, 63]
(iii) CNN [17, 19–21, 54, 64, 65, 70, 73–76, 79, 81, 82, 95, 96]
(iv) LSTM [29, 69]
(v) CRBM [53]
(vi) Autoencoder network [50, 62]
(vii) Generative adversarial networks [66, 67]
(viii) HDMF [71, 72]
(ix) NFSC [78] |
| | Pros | (i) works better on small data
(ii) low implementation cost | (i) simple pre-processing
(ii) high accuracy and efficiency
(iii) adaptive to different applications |
| | Cons | (i) time demanding
(ii) complex feature engineering
(iii) depends heavily on the representation of the data
(iv) prone to curse of dimensionality | (i) demanding large amounts of data
(ii) high hardware cost |
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