The algorithm AE_(11) of learning from examples

We first put forward the idea of a positive extension matrix (PEM) on paper. Then, an algorithm, AE_ 11, was built with the aid of the PEM. Finally, we made the comparisons of our experimental results and the final result was fairly satisfying.

Wireless Device Connection Problems and Design Solutions

Users, especially the non-expert users, commonly experience problems when connecting multiple devices with interoperability. While studies on multiple device connections are mostly concentrated on spontaneous device association techniques with a focus on security aspects, the research on user interaction for device connection is still limited. More research into understanding people is needed for designers to devise usable techniques. This research applies the Research-through-Design method and studies the non-expert users＇ interactions in establishing wireless connections between devices. The ＂Learning from Examples＂ concept is adopted to develop a study focus line by learning from the expert users＇ interaction with devices. This focus line is then used for guiding researchers to explore the non-expert users＇ difficulties at each stage of the focus line. Finally, the Research-through-Design approach is used to understand the users＇ difficulties, gain insights to design problems and suggest usable solutions. When connecting a device, the user is required to manage not only the device＇s functionality but also the interaction between devices. Based on learning from failures, an important insight is found that the existing design approach to improve single-device interaction issues, such as improvements to graphical user interfaces or computer guidance, cannot help users to handle problems between multiple devices. This study finally proposes a desirable user-device interaction in which images of two devices function together with a system image to provide the user with feedback on the status of the connection, which allows them to infer any required actions.

Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems

Particle swarm optimization(PSO)is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation.However,PSO still has certain deficiencies,such as a poor trade-off between exploration and exploitation and premature convergence.Hence,this paper proposes a dual-stage hybrid learning particle swarm optimization(DHLPSO).In the algorithm,the iterative process is partitioned into two stages.The learning strategy used at each stage emphasizes exploration and exploitation,respectively.In the first stage,to increase population variety,a Manhattan distance based learning strategy is proposed.In this strategy,each particle chooses the furthest Manhattan distance particle and a better particle for learning.In the second stage,an excellent example learning strategy is adopted to perform local optimization operations on the population,in which each particle learns from the global optimal particle and a better particle.Utilizing the Gaussian mutation strategy,the algorithm’s searchability in particular multimodal functions is significantly enhanced.On benchmark functions from CEC 2013,DHLPSO is evaluated alongside other PSO variants already in existence.The comparison results clearly demonstrate that,compared to other cutting-edge PSO variations,DHLPSO implements highly competitive performance in handling global optimization problems.

The Minimum Feature Subset Selection Problem

In applications of learning from examples to real-world tasks, feature subset selection is important to speed up training and to improve generalization performance. ideally, an inductive algorithm should use subset of features as small as possible. In this paper however, the authors show that the problem of selecting the minimum subset of features is NP-hard. The paper then presents a greedy algorithm for feature subset selection. The result of running the greedy algorithm on hand-written numeral recognition problem is also given.