Bayes and Naive-Bayes Classifier

The employment of Artificial Neural Networks to the classification of meteorological data has been considered in previous papers and found to offer promising results. We compare the performance of the Bayesian Classifier with two different Neural Network architectures. The classifiers were used to segment images of cloud into four different meteorological classes on the basis of spectral-textur...

In this note we record a simple proof of a beautiful result of J.D.S. Jones, stating that the Mahowald root invariant of a stable homotopy class a has dimension at least twice that of c~. This result is a natural strengthening of the Kahn-Priddy theorem. Our contribution is simply to provide a postcard-length proof of the key" diagram (3.i) (which occurs on p. 481 of [2]), but we take the oppor...

Social networks play an important role in Web 2.0. For many users establishing contacts and staying in touch in the virtual world is more than just a spare time filler. In social networks like Facebook they provide much information about themselves in user profiles. Also for online dating the focus is on establishing new contacts. In general, three types of dating sites can be distinguished: mo...

Student dropout occurs quite often in universities providing distance education. The scope of this research is to study whether the usage of machine learning techniques can be useful in dealing with this problem. Subsequently, an attempt was made to identifying the most appropriate learning algorithm for the prediction of students' dropout. A number of experiments have taken place with data pro...

This paper presents the participation of #WarTeam in Task 6 of SemEval2017 with a system classifying humor by comparing and ranking tweets. The training data consists of annotated tweets from the @midnight TV show. #WarTeam’s system uses a neural network (TensorFlow) having inputs from a Naïve Bayes humor classifier and a sentiment analyzer.

In dialogue management for statistical spoken dialogue systems, an agent learns a policy that maps a belief state to an action for the system to perform. Efficient exploration is key to successful dialogue policy estimation. Current deep reinforcement learning methods are very promising but rely on ε-greedy exploration, which is not as sample efficient as methods that use uncertainty estimates,...

Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common examples of such diseases, where the mutational load in the cancerous/viral population increases over time. In these cases, selective pressures are often observed along with competition, cooperation and parasitism among d...

Bayesian Reinforcement Learning (BRL) agents aim to maximise the expected collected rewards obtained when interacting with an unknown Markov Decision Process (MDP) while using some prior knowledge. State-of-the-art BRL agents rely on frequent updates of the belief on the MDP, as new observations of the environment are made. This offers theoretical guarantees to converge to an optimum, but is co...