0000000000273678
AUTHOR
Radu Crețulescu
On Hagelbarger’s and Shannon’s matching pennies playing machines
Abstract In the 1950s, Hagelbarger’s Sequence Extrapolating Robot (SEER) and Shannon’s Mind-Reading Machine (MRM) were the state-of-the-art research results in playing the well-known “matching pennies” game. In our research we perform a software implementation for both machines in order to test the common statement that MRM, even simpler, beats SEER. Also, we propose a simple contextual predictor (SCP) and use it to compete with SEER and MRM. As expected, experimental results proves the claimed MRM superiority over SEER and even the SCP’s superiority over both SEER and MRM. At the end, we draw some conclusions and propose further research ideas, like the use of mixing models methods and the…
An Extension of the VSM Documents Representation using Word Embedding
Abstract In this paper, we will present experiments that try to integrate the power of Word Embedding representation in real problems for documents classification. Word Embedding is a new tendency used in the natural language processing domain that tries to represent each word from the document in a vector format. This representation embeds the semantically context in that the word occurs more frequently. We include this new representation in a classical VSM document representation and evaluate it using a learning algorithm based on the Support Vector Machine. This new added information makes the classification to be more difficult because it increases the learning time and the memory neede…