In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation. Keywords—machine learning; deep learning; TensorFlow; Deep Learning4j; H2O; MNIST; multicore CPU; GPU.

Ex Output:
Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single- Threaded and Multi-Threaded Modes


Ex Input:
Diagnosis of liver infection at preliminary stage is important for better treatment. In today’s scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naïve Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.

Ex Output:
NeuroSVM: A Graphical User Interface for Identification of Liver Patients


Ex Input:
We present a new method for estimating the expected return of a POMDP from experi­ ence. The estimator does not assume any knowledge of the POMDP, can estimate the returns for finite state controllers, allows ex­ perience to be gathered from arbitrary se­ quences of policies, and estimates the return for any new policy. We motivate the estima­ tor from function-approximation and impor­ tance sampling points-of-view and derive its bias and variance. Although the estimator is biased, it has low variance and the bias is of­ ten irrelevant when the estimator is used for pair-wise comparisons. We conclude by ex­ tending the estimator to policies with mem­ ory and compare its performance in a greedy search algorithm to the REINFORCE algo­ rithm showing an order of magnitude reduc­ tion in the number of trials required.

Ex Output:

Policy Improvement for POMDPs using Normalized Importance Sampling