Friday, December 8, 2017

Matlab-Supervised and Unsupervised Learning

Supervised Learning

The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data.Supervised learning uses classifcation and regression techniques to develop predictive models.

  • Classifcation techniques predict discrete responses—for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classifcation models classify input data into categories. Typical applications include medical imaging, speech recognition, and credit scoring.
  • Regression techniques predict continuous responses— for example, changes in temperature or fuctuations in  power demand. Typical applications include electricity load forecasting and algorithmic trading.

Inti dari Supervised  Learning adalah tersedianya input dan target sehingga tugas machine learning adalah mencari tahu/memodelkan persamaan berdasarkan input dan target yang telah tersedia

Unsupervised Learning

Unsupervised learning fnds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses.
  • Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to fnd hidden patterns orgroupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition.
Inti dari Unsupervised Learning adalah sistem akan diberikan target berupa jumlah grouping, maka tugas machine learning adalah mengelompokan input-input mana saja yang akan masuk dalam group

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