Perceptron

Dalam ilmu pemelajaran mesin, perceptron adalah algoritme pada pemelajaran terbimbing untuk melakukan klasifikasi biner (dua jenis). Klasifikasi biner menggunakan suatu fungsi yang menentukan suatu data termasuk dalam suatu kelompok atau tidak.[1] Ia termasuk jenis pengklasifikasi linear, yaitu algoritme klasifikasi yang memprediksi dengan fungsi linear yang tersusun dari himpunan bobot dengan vektor fitur.

Definisi

Dalam konsep modern, perceptron adalah algoritme untuk mempelajari suatu pengklasifikasi biner yang disebut fungsi ambang batas, yaitu fungsi yang memetakan input (vektor bernilai riil) ke nilai (nilai biner):

dengan adalah vektor bobot (weight) bernilai riil dan adalah produk skalar dengan adalah jumlah input ke perceptron dan adalah bias. Nilai bias menggeser batasan keputusan menjauh dari titik asal dan tidak bergantung pada nilai input.

Nilai (0 atau 1) dipakai untuk mengklasifikasikan sebagai kelompok positif atau negatif (untuk kasus klasifikasi biner).

Dalam konteks jaringan saraf tiruan, perceptron adalah saraf tiruan yang menggunakan fungsi tangga Heaviside sebagai fungsi aktivasi. Algoritme perceptron juga disebut perceptron lapis tunggal (single-layer perceptron) untuk membedakannya dari perceptron lapis banyak (multilayer perceptron) yang dipakai untuk jaringan saraf tiruan yang lebih kompleks. Sebagai pengklasifikasi linear, perceptron lapis tunggal adalah jaringan saraf tiruan langkah depan (feed-forward) paling sederhana.

Bacaan lebih lanjut

  • Aizerman, M. A., Braverman, E. M., dan Lev I. Rozonoer. 1964. "Theoretical foundations of the potential function method in pattern recognition learning". Automation and Remote Control, 25: 821–837.
  • Collins, M. 2002. "Discriminative training methods for hidden Markov models: Theory and experiments with the perceptron algorithm" dalam Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '02).
  • Gallant, S. I. 1990. "Perceptron-based learning algorithms". IEEE Transactions on Neural Networks, 1 (2), hal. 179–191.
  • Minsky M. L. dan Papert S. A. 1969. Perceptrons. Cambridge: MIT Press.
  • Mohri, Mehryar dan Rostamizadeh, Afshin. 2013. "Perceptron Mistake Bounds". arXiv:1305.0208 [cs.LG].
  • Novikoff, A. B. 1962. "On convergence proofs on perceptrons". Symposium on the Mathematical Theory of Automata, 12, hal. 615–622.
  • Rosenblatt, Frank. 1958. "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain". Psychological Review, 65 (6), hal. 386–408. DOI:10.1037/h0042519.
  • Rosenblatt, Frank. 1962. Principles of Neurodynamics. Washington: Spartan Books.
  • Widrow, B. dan Lehr, M. A. 1990. "30 years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation". Proc. IEEE, 78 (9), hal. 1415–1442.
  • Yin, Hongfeng. 1996. "Perceptron-Based Algorithms and Analysis". Spectrum Library, Concordia University, Kanada

Referensi

  1. ^ Freund, Y.; Schapire, R. E. (1999). "Large margin classification using the perceptron algorithm" (PDF). Machine Learning. 37 (3): 277–296. doi:10.1023/A:1007662407062. 


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