63,271 research outputs found
Testing Top Monotonicity
Top monotonicity is a relaxation of various well-known domain restrictions
such as single-peaked and single-crossing for which negative impossibility
results are circumvented and for which the median-voter theorem still holds. We
examine the problem of testing top monotonicity and present a characterization
of top monotonicity with respect to non-betweenness constraints. We then extend
the definition of top monotonicity to partial orders and show that testing top
monotonicity of partial orders is NP-complete
Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting
The smart metering infrastructure has changed how electricity is measured in
both residential and industrial application. The large amount of data collected
by smart meter per day provides a huge potential for analytics to support the
operation of a smart grid, an example of which is energy demand forecasting.
Short term energy forecasting can be used by utilities to assess if any
forecasted peak energy demand would have an adverse effect on the power system
transmission and distribution infrastructure. It can also help in load
scheduling and demand side management. Many techniques have been proposed to
forecast time series including Support Vector Machine, Artificial Neural
Network and Deep Learning. In this work we use Long Short Term Memory
architecture to forecast 3-day ahead energy demand across each month in the
year. The results show that 3-day ahead demand can be accurately forecasted
with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper
proposes way to quantify the time as a feature to be used in the training phase
which is shown to affect the network performance
A model for mobile content filtering on non-interactive recommendation systems
To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems
Improved Reinforcement Learning with Curriculum
Humans tend to learn complex abstract concepts faster if examples are
presented in a structured manner. For instance, when learning how to play a
board game, usually one of the first concepts learned is how the game ends,
i.e. the actions that lead to a terminal state (win, lose or draw). The
advantage of learning end-games first is that once the actions which lead to a
terminal state are understood, it becomes possible to incrementally learn the
consequences of actions that are further away from a terminal state - we call
this an end-game-first curriculum. Currently the state-of-the-art machine
learning player for general board games, AlphaZero by Google DeepMind, does not
employ a structured training curriculum; instead learning from the entire game
at all times. By employing an end-game-first training curriculum to train an
AlphaZero inspired player, we empirically show that the rate of learning of an
artificial player can be improved during the early stages of training when
compared to a player not using a training curriculum.Comment: Draft prior to submission to IEEE Trans on Games. Changed paper
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