데이터 마이닝 입문 2. 알고리즘 정리

데이터 마이닝의 알고리즘은 크게 아래의 4가지로 나뉜다.

물론 내가 준비하는 연구 논문에 아래의 알고리즘을 새롭게 만든것도 있고, 아닌 것도 있다.

1. SVM (Support Vector Mahine)

기계 학습의 과정에서 SVM, 서포트 벡터 머신은 학습알고리즘을 통해 기억하는 습득 모델이다.

아래는 간단한 위키의 설명.

In machine learning, support vector machines (SVMs, also support vector networks^[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

In addition to performing linear classification, SVMs can efficiently perform non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

간단하지는 않지만..

2. EM (Expectation Maximization)
3. K-Means (K-평균)
4. CF (Collaborative Filtering)