2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)最新文献_第3页

Evaluating Robustness of Referring Expression Generation Algorithms 引用表达式生成算法的鲁棒性评价

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.10

Pablo Duboue, Martín Ariel Domínguez, Paula Estrella

引用次数: 3

An Extension of Fuzzy C-Means Applied to Spot Recognition in Images of Two-Dimensional Electrophoresis 模糊c均值扩展在二维电泳图像斑点识别中的应用

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.16

Marlon Dias, Geancarlo Maydana, M. Aguiar

引用次数: 1

An Algorithm for Bounding Error in Estimates of Genome Copy Number Variations Using SNP Array Technology 利用SNP阵列技术估计基因组拷贝数变异的边界误差算法

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.40

Jorge Muñoz, Y. Shmaliy, Roberto Olivera

引用次数: 0

Recognition of Moving Objects in Videos of Moving Camera with Harris Attributes 基于Harris属性的运动摄像机视频中运动物体的识别

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.13

A. Nozari, S. Hoseini

引用次数: 1

Diminishing Prototype Size for k-Nearest Neighbors Classification k近邻分类的原型尺寸递减

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.27

M. Samadpour, H. Parvin, F. Rad

{"title":"Diminishing Prototype Size for k-Nearest Neighbors Classification","authors":"M. Samadpour, H. Parvin, F. Rad","doi":"10.1109/MICAI.2015.27","DOIUrl":"https://doi.org/10.1109/MICAI.2015.27","url":null,"abstract":"In this paper, a new classification method based on k-Nearest Neighbor (kNN) lazy classifier is proposed. This method leverages the clustering concept to reduce the size of the training set in kNN classifier and also in order to enhance its performance in terms of time complexity. The new approach is called Modified Nearest Neighbor Classifier Based on Clustering (MNNCBC). Inspiring the traditional lazy k-NN algorithm, the main idea is to classify a test instance based on the tags of its k nearest neighbors. In MNNCBC, the training set is first grouped into a small number of partitions. By obtaining a number of partitions employing several runnings of a simple clustering algorithm, MNNCBC algorithm extracts a large number of clusters out of those partitions. Then, a label is assigned to the center of each cluster produced in the previous step. The assignment is determined with use of the majority vote mechanism between the class labels of the patterns in each cluster. MNNCBC algorithm iteratively inserts a cluster into a pool of the selected clusters that are considered as the training set of the final 1-NN classifier as long as the accuracy of 1-NN classifier over a set of patterns included the training set and the validation set improves. The selected set of the most accurate clusters are considered as the training set of proposed 1-NN classifier. After that, the class label of a new test sample is determined according to the class label of the nearest cluster center. While kNN lazy classifier is computationally expensive, MNNCBC classifier reduces its computational complexity by a multiplier of 1/k. So MNNCBC classifier is about k times faster than kNN classifier. MNNCBC is evaluated on some real datasets from UCI repository. Empirical results show that MNNCBC has an excellent improvement in terms of both accuracy and time complexity in comparison with kNN classifier.","PeriodicalId":448255,"journal":{"name":"2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122069151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Improving RL Speed by Adding Unseen Experiences via Operators Inspired by Genetic Algorithm Operators Enriched by Chaotic Random Generator 由混沌随机发生器丰富的遗传算子启发的算子加入未知经验提高强化学习速度

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.21

Mostafa Rafiei, M. Sina

{"title":"Improving RL Speed by Adding Unseen Experiences via Operators Inspired by Genetic Algorithm Operators Enriched by Chaotic Random Generator","authors":"Mostafa Rafiei, M. Sina","doi":"10.1109/MICAI.2015.21","DOIUrl":"https://doi.org/10.1109/MICAI.2015.21","url":null,"abstract":"In many Multi-Agent Systems, under-education agents investigate their environments to discover their target(s). Any agent can also learn its strategy. In multi-task learning, one agent studies a set of related problems together simultaneously, by a common model. In reinforcement learning exploration phase, it is necessary to introduce a process of trial and error to learn better rewards obtained from environment. To reach this end, anyone can typically employ the uniform pseudorandom number generator in exploration period. On the other hand, it is predictable that chaotic sources also offer a random-like series comparable to stochastic ones. It is useful in multi-task reinforcement learning, to use teammate agents' experience by doing simple interactions between each other. We employ the past experiences of agents to enhance performance of multi-task learning in a nondeterministic environment. Communications are created by operators of evolutionary algorithm. In this paper we have also employed the chaotic generator in the exploration phase of reinforcement learning in a nondeterministic maze problem. We obtained interesting results in the maze problem.","PeriodicalId":448255,"journal":{"name":"2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116641212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SSPCO Optimization Algorithm (See-See Partridge Chicks Optimization) SSPCO优化算法(参见Partridge Chicks Optimization)

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.22

R. Omidvar, H. Parvin, F. Rad

引用次数: 11

Elicitation Process and Knowledge Structuring: A Conceptual Framework for Biodiversity 启发过程和知识结构:生物多样性的概念框架

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.17

Andréa Corrêa Flôres Albuquerque, José Laurindo Campos dos Santos, Alberto Nogueira de Castro Júnior

引用次数: 1

Any Object Tracking and Following by a Flying Drone 任何物体跟踪和跟随飞行无人机

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.12

R. Barták, Adam Vykovsky

引用次数: 33

A Parallel Solver for Markov Decision Process in Crowd Simulations 人群模拟中马尔可夫决策过程的并行求解器

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.23

Sergio Ruiz, Benjamín Hernández

引用次数: 21