Latest Features for Machine Learning

최신 기능

머신 러닝을 위한 최신 MATLAB에 대해 알아보기

New Optimize model type and hyperparameters simultaneously
Automatically tune hyperparameters using Bayesian optimization
Automatically select a subset of relevant features using techniques like neighborhood component analysis (NCA) and feature ranking
Parallelize the execution of automated optimization methods on multiple cores using Parallel Computing Toolbox, and scale to clouds and clusters using MATLAB Parallel Server

Leverage commonly used algorithms for classification and regression, such as linear and generalized linear models, support vector machines, decision trees, ensemble methods, and more
Use popular clustering algorithms including k-means, k-mediods, hierarchical clustering, Gaussian mixture, and Hidden Markov models
New Use density-based spatial clustering of applications with noise (DBSCAN) and spectral clustering of arbitrary shapes
Run statistical and machine learning computations faster than with open-source tools

Explore the structure of your data and relationships between features through scatter plots, box plots, dendrograms, and other standard statistical visualizations
Use advanced dimensionality reduction algorithms like Stochastic Neighbor Embedding (t-SNE)
Visualize high-density data with improved scatter plots in the Classification Learner app
New Create confusion matrices from tall arrays

Automatically generate C/C++ code for many popular classification, regression, and clustering algorithms
New Deploy to devices with limited memory and/or power using fixed-point arithmetic
New Update parameters of deployed models such as SVM, linear models, and decision trees, without regenerating C/C++ prediction code

베이지안 최적화를 사용하여 하이퍼파라미터를 자동으로 조정
NCA(Neighborhood Component Analysis) 같은 기법을 사용하여 관련 기능의 서브셋을 자동으로 선택
신규 스파스 필터링(sparse filtering)과 RICA(reconstruction independent component analysis)를 사용하여 비지도(unsupervised) 특징 학습 수행

MATLAB®과 Microsoft® R Open (3.4.1) 및 Python 용 Intel ® Distribution (2018)을 여러 가지 프로그래밍 및 머신러닝 작업에서 비교합니다.

선형 및 일반화된 선형 모델, SVM(Support Vector Machine), 결정 트리, 앙상블 메서드 등과 같은 분류 및 회귀를 위한 알고리즘에 일반적으로 활용
신규 잘 알려진 클러스터링 알고리즘(예: k-평균, k-대푯값, 계층 클러스터링, 가우시안 혼합, Hidden Markov 모델 포함) 사용
오픈 소스 툴보다 빠른 통계 및 머신 러닝 연산 실행

30일 동안 사용해 보십시오.