Classification is one of the most important tasks in Machine Learning (ML)
and with recent advancements in artificial intelligence (AI) it is important to
find efficient ways to implement it. Generally, the choice of classification
algorithm depends on the data it is dealing with, and accuracy of the algorithm
depends on the hyperparameters it is tuned with. One way is to check the
accuracy of the algorithms by executing it with different hyperparameters
serially and then selecting the parameters that give the highest accuracy to
predict the final output. This paper proposes another way where the algorithm
is parallelly trained with different hyperparameters to reduce the execution
time. In the end, results from all the trained variations of the algorithms are
ensembled to exploit the parallelism and improve the accuracy of prediction.
Python multiprocessing is used to test this hypothesis with different
classification algorithms such as K-Nearest Neighbors (KNN), Support Vector
Machines (SVM), random forest and decision tree and reviews factors affecting
parallelism. Ensembled output considers the predictions from all processes and
final class is the one predicted by maximum number of processes. Doing this
increases the reliability of predictions. We conclude that ensembling improves
accuracy and multiprocessing reduces execution time for selected algorithms.

本文研究了如何通过并行训练不同超参数的分类算法来减少执行时间，并使用 Python 多进程来测试这一假设，通过集成所有进程的预测来提高准确性。结果表明，集成方法提高了准确性，多进程减少了执行时间。

多进程数据分类

Data Classification With Multiprocessing

This study focuses on designing and developing a mathematically based
quadcopter rotational dynamics simulation framework for testing reinforcement
learning (RL) algorithms in many flexible configurations. The design of the
simulation framework aims to simulate both linear and nonlinear representations
of a quadcopter by solving initial value problems for ordinary differential
equation (ODE) systems. In addition, the simulation environment is capable of
making the simulation deterministic/stochastic by adding random Gaussian noise
in the forms of process and measurement noises. In order to ensure that the
scope of this simulation environment is not limited only with our own RL
algorithms, the simulation environment has been expanded to be compatible with
the OpenAI Gym toolkit. The framework also supports multiprocessing
capabilities to run simulation environments simultaneously in parallel. To test
these capabilities, many state-of-the-art deep RL algorithms were trained in
this simulation framework and the results were compared in detail.

本研究旨在设计和开发一个数学基础的四旋翼旋转动态仿真框架，以测试强化学习算法在各种灵活的配置下的表现，该框架利用初值问题求解常微分方程（ODE）系统来模拟四旋翼的线性和非线性表现，并通过添加随机高斯噪声使仿真变得确定性 / 随机性，同时该框架还支持多进程并行运行。