Tractable Bayesian Classification – Multi-view Markov Decision Processes (MDPs) can be defined on a set of parameters, i.e. the number of variables and the variables of the learning process. However, this model is challenging to scale to large datasets due to the large amounts of labeled data. In this paper, we propose a new model to deal with this challenge. The parameter-based, multi-view MDP approach is derived from a novel multi-view Bayesian Decision Processes (MDP) model that is based on a large set of labeled data. The parameter-based MDP model is formulated to process the input data in a linear and non-convex manner, where the MDP structure is generated by a simple linear transformation (e.g. the distribution of variables). The proposed approach is tested on a large set of datasets with a large number of variables. In particular, it achieves an accuracy of 97.5% where the state of the art is 98.6%. In addition, the proposed approach is able to handle large-scale tasks such as classification and summarization.
This paper shows how language-independent speech segmentation can be used to learn word-level semantic representations of sentences. Since words tend to be highly relevant in contexts, word level representations are often learned from word semantic annotations. However, word information is highly correlated with context, which can make semantic representations poorly learned. Our goal is to learn word word semantic representations from both word semantic annotations and word context. To the best of our knowledge, this is the first time we have done this for sentence learning. By leveraging neural networks to learn word semantic representations, we model the context in a supervised manner, and then use word level semantic annotations as the learning model to learn word semantic representations. We show that the learned word semantic representations form the first of many promising word semantic representation models that can be used for sentence learning.
The Multi-Horizon Approach to Learning, Solving and Solving Rubik’s Revenge
The Spatial Proximal Projection for Kernelized Linear Discriminant Analysis
Tractable Bayesian Classification
Learning to Rank by Minimising the Ranker
A Sentence Embedding for Semantic Role Induction in Compositional and Compositional Word SegmentationThis paper shows how language-independent speech segmentation can be used to learn word-level semantic representations of sentences. Since words tend to be highly relevant in contexts, word level representations are often learned from word semantic annotations. However, word information is highly correlated with context, which can make semantic representations poorly learned. Our goal is to learn word word semantic representations from both word semantic annotations and word context. To the best of our knowledge, this is the first time we have done this for sentence learning. By leveraging neural networks to learn word semantic representations, we model the context in a supervised manner, and then use word level semantic annotations as the learning model to learn word semantic representations. We show that the learned word semantic representations form the first of many promising word semantic representation models that can be used for sentence learning.