| 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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