23 citations · 73 across the 19 of their papers we have counts for
5 papers · 1 filter
Understanding and Accelerating EM Algorithm's Convergence by Fair Competition Principle and Rate-Verisimilitude Function
Chenguang Lu
Why can the Expectation-Maximization (EM) algorithm for mixture models converge? Why can different initial parameters cause various convergence difficulties? The Q-L synchronizatio…
The CM Algorithm for the Maximum Mutual Information Classifications of Unseen Instances
Chenguang Lu
The Maximum Mutual Information (MMI) criterion is different from the Least Error Rate (LER) criterion. It can reduce failing to report small probability events. This paper introduc…
From the EM Algorithm to the CM-EM Algorithm for Global Convergence of Mixture Models
Chenguang Lu
The Expectation-Maximization (EM) algorithm for mixture models often results in slow or invalid convergence. The popular convergence proof affirms that the likelihood increases wit…
Semantic Channel and Shannon's Channel Mutually Match for Multi-Label Classification
Chenguang Lu
A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. Label learning is to let semantic channels match Sh…
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning
Chenguang Lu
A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. By the third kind of Bayes' theorem, we can directl…