Multi-Objective Machine Learning Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful deve... Y Jin - 《Studies in Computational Intelligence》 被引量: 171发表: 2006年 Machine learning based decision supp...
In this paper we proposed a scheduling algorithm based on a Machine Learning Box for asymptotic requests: we described the model we built, then the implementation in a simulation environment of the novel algorithm and of 8 heuristics both for BoTs and DAGs and last, we performed some tests for...
Machine Learning (ML) is an essential technology for adapting parameters under changing conditions. Using ML, this paper presents an approach that allows an optimized configuration of the planning parameters depending on the data. The method is evaluated based on real-world data sets from the ...
Currently, machine learning algorithms have shown outstanding performance in solving the classification problem. Similarly, given the sufficient amount of related network statistics, we may anticipate a well-trained machine learning model (e.g., deep learning model) can provide an accurate mapping from ...
Furthermore, convolutional neural networks and Boltzmann machine networks, and many others are used to solve machine learning problems. ANNs learn in the same way that humans do: from past experiences. They require data to learn, and the more data they get, the more accurate the prediction. ...
In scheduling problems with learning effects, most research assumes that processing times are deterministic. This paper studies a single-machine scheduling problem with a position-based learning effect and fuzzy processing times where the objective is to minimize the makespan. The position-based learning...
For example, if you had an anomaly in your historical contact volume, and you don't want the machine-learning model to use that anomaly in building a forecast, you can modify the historical data and then when the new forecasts are run, the new forecasts are not incorporate that d...
The scheduler replaces Anki's built-in scheduler and schedules the cards according to the FSRS algorithm. The optimizer uses machine learning to learn your memory patterns and finds parameters that best fit your review history. For details about the working of the optimizer, please readthe mechani...
我们采用一些优化方法使得环境更快。一方面,我们试图保持state表征更小,另一方面,多种编码提升方式计算速度。因此,保证状态表征存在memory中并且只更新每一步需要更新的属性,而不是重新计算。 Method 这一部分提供我们方法的详细信息,描述实现方案。 Action Selection 我们给出的environment给出了一个基于矩阵的表形的状态...
For example, memory is comparatively expensive in terms of chip area and may therefore be a limitation in many applications. Generally, power consumption is the major limiting factor, but we will choose to minimize the amount of hardware resources (i.e., the number of PEs, memories, and ...