Training nuclei detection algorithms with simple annotations

Training Nuclei Detection Algorithms with Simple Annotations

BACKGROUND Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. METHODS We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, e...

Exploiting Partial Annotations with EM Training

For many NLP tasks, EM-trained HMMs are the common models. However, in order to escape local maxima and find the best model, we need to start with a good initial model. Researchers suggested repeated random restarts or constraints that guide the model evolution. Neither approach is ideal. Restarts are time-intensive, and most constraint-based approaches require serious re-engineering or externa...

Simple Learning Algorithms for Training Support Vector Machines

Support Vector Machines SVMs have proven to be highly e ective for learning many real world datasets but have failed to establish them selves as common machine learning tools This is partly due to the fact that they are not easy to implement and their standard imple mentation requires the use of optimization packages In this paper we present simple iterative algorithms for training support vect...

Browsing a Video with Simple Constrained Queries over Fuzzy Annotations

Self-Training PCFG Grammars with Latent Annotations Across Languages

We investigate the effectiveness of selftraining PCFG grammars with latent annotations (PCFG-LA) for parsing languages with different amounts of labeled training data. Compared to Charniak’s lexicalized parser, the PCFG-LA parser was more effectively adapted to a language for which parsing has been less well developed (i.e., Chinese) and benefited more from selftraining. We show for the first t...