TLDRai.com Too Long; Didn't Read AI TLDWai.com Too Long; Didn't Watch AI
በ AI ያልተገደበ ማጠቃለያዎችን ያድርጉ!
ወደ PRO አሻሽል። US$ 7.0/m
ምንም የተከለከሉ ተግባራት የሉም

Introduction to Machine Learning: Module 6.9 GMM-EM hyper-parameter tuning

The speaker is discussing the use of Bayesian Information Criterion (BIC) in Gaussian Mixture Model Expectation-Maximization (GMM-EM) clustering. The BIC measures how well a model fits the data, and it helps determine the number of clusters in a dataset. GMM-EM can capture differences in probability among clusters, which can impact the choice of the number of clusters. In contrast, K-means assumes equally likely clusters, while GMM-EM can encode probabilities of belonging to different clusters. The lecture highlights similarities and differences between these two clustering algorithms.
PRO ተጠቃሚዎች ከፍተኛ ጥራት ማጠቃለያዎችን ያገኛሉ
ወደ PRO አሻሽል። US$ 7.0/m
ምንም የተከለከሉ ተግባራት የሉም
የአካባቢ ቪዲዮን ማጠቃለል የመስመር ላይ ቪዲዮን ማጠቃለል

ከተጨማሪ ባህሪያት ጋር የተሻሉ የጥራት ውጤቶችን ያግኙ

PRO ይሁኑ


ተዛማጅ ማጠቃለያዎች