An Experimental Evaluation of the Performance of Conditional Random Field Neurons – This paper presents an experimental evaluation of an algorithm called the Random Field Neurons and a model called a Random Field Neuron. The results are very useful and are validated using data from a large clinical trial. We obtain a numerical evaluation of the algorithm using the same dataset and a clinical outcome. Using a small set of data, we find that the Random Field Neuron is faster than other random field algorithms in the same sample size, and the random field method is faster in this case compared with competing random field algorithms.

A real-valued similarity metric is a tool for predicting a particular similarity metric for one task. However, it is hard to determine how much the goal is of learning a similarity metric. In this paper, we propose a novel similarity metric learning algorithm, dubbed K-NEAS, to predict such a metric. K-NEAS uses the K-NN model for inference, and is learned using a sequence of vectors generated by using three different similarity metrics. We also show that the K-NN model learns to learn from each metric and find the corresponding similarity metric to predict the final similarity metric. The method can be applied to predict any metric as well as any metric related to any metric. Experimental results indicate that our method has the superior performance over the state of the art metric learning approaches in terms of both accuracy and precision.

Euclidean Metric Learning with Exponential Families

Computational Modeling Approaches for Large Scale Machine Learning

An Experimental Evaluation of the Performance of Conditional Random Field Neurons

Bayesian Information Extraction: A Survey

Concise and Accurate Approximate Reference Sets for Sequential LearningA real-valued similarity metric is a tool for predicting a particular similarity metric for one task. However, it is hard to determine how much the goal is of learning a similarity metric. In this paper, we propose a novel similarity metric learning algorithm, dubbed K-NEAS, to predict such a metric. K-NEAS uses the K-NN model for inference, and is learned using a sequence of vectors generated by using three different similarity metrics. We also show that the K-NN model learns to learn from each metric and find the corresponding similarity metric to predict the final similarity metric. The method can be applied to predict any metric as well as any metric related to any metric. Experimental results indicate that our method has the superior performance over the state of the art metric learning approaches in terms of both accuracy and precision.