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检索条件"任意字段=6th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2015"

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6th international conference on pattern recognition and machine intelligence, premi 2015

6th International Conference on Pattern Recognition and Mach...

引用

6th international conference on pattern recognition and machine intelligence, premi 2015

the proceedings contain 55 papers. the special focus in this conference is on pattern recognition and machine intelligence. the topics include: Recent Advances in Recommender Systems and Future Directions;On the Number of Rules and Conditions in Mining Data with Attribute-Concept Values and "Do Not Care" Conditions;Simplifying Contextual Structures;Towards a Robust Scale Invariant Feature Correspondence;Hierarchical Agglomerative Method for Improving NPS;A New Linear Discriminant Analysis Method to Address the Over-Reducing Problem;Procedural Generation of Adjustable Terrain for Application in Computer Games Using 2D Maps;Fixed Point Learning Based 3D Conversion of 2D Videos;Fast and Accurate Foreground Background Separation for Video Surveillance;Enumeration of Shortest Isothetic Paths Inside a Digital Object;Modified Exemplar-Based Image Inpainting via Primal-Dual Optimization;A Novel Approach for Image Super Resolution Using Kernel Methods;Generation of Random Triangular Digital Curves Using Combinatorial Techniques;Tackling Curse of Dimensionality for Efficient Content Based Image Retrieval;Face Profile View Retrieval Using Time of Flight Camera Image Analysis;Context-Based Semantic Tagging of Multimedia Data;Improved Simulation of Holography Based on Stereoscopy and Face Tracking;Head Pose Tracking from RGBD Sensor Based on Direct Motion Estimation;A Novel Hybrid CNN-AIS Visual pattern recognition Engine;Modified Orthogonal Neighborhood Preserving Projection for Face recognition;An Optimal Greedy Approximate Nearest Neighbor Method in Statistical pattern recognition.

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Neural Topic Model with Distance Awareness 27th

Neural Topic Model with Distance Awareness

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27th international conference on pattern recognition, ICPR 2024

作者： Chen, Shangyu Zhao, He Huynh, Viet Phung, Dinh Cai, Jianfei FIT-DSAI Monash University Exhibition Walk ClaytonVIC3168 Australia CSIRO’s Data61 Garden St EveleighNSW2015 Australia School of Science Edith Cowan University 270 Joondalup Dr JoondalupWA6027 Australia

ISBN: (纸本)9783031781889

Neural topic models (NTMs) have shown their success in topic modeling with a wide range of applications in text analysis. NTMs based on generative models prioritize document representations with good reconstruction capabilities, but they are they are insufficient in preserving distances between documents in the topic space. To bridge this gap, inspired by manifold learning, we propose a neural topic model that enables the reflection of word-to-word relationships onto topic-to-topic associations. this is achieved by approximating the distances between documents in the word space within the topic space. Extensive experiments demonstrate that the proposed model outperforms state-of-the-art NTMs in improving the quality of learned topics, as evidenced by metrics such as purity, diversity, coherence. Beyond that, the model can provide more interpretable low dimensional visualizations of documents. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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pattern recognition and machine intelligence 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Marzena Kryszkiewicz Henryk Rybinski Sanghamitra Bandyopadhyay Sankar K. Pal

ISBN: (数字)9783319199412

ISBN: (纸本)9783319199405

this book constitutes the proceedings of the 6th international conference on pattern recognition and machine intelligence, premi 2015, held in Warsaw, Poland, in June/July 2015. the total of 53 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 90 submissions. they were organized in topical sections named: foundations of machine learning; image processing; image retrieval; image tracking; pattern recognition; data mining techniques for large scale data; fuzzy computing; rough sets; bioinformatics; and applications of artificial intelligence.

关键词： Computation by Abstract Devices Artificial intelligence pattern recognition Information Systems Applications (incl. Internet) Data Mining and Knowledge Discovery Computational Biology/Bioinformatics

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Using patterns in computer go 6th

Using patterns in computer go

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6th international conference on pattern recognition and machine intelligence, premi 2015

作者： Śliwa, Leszek Stanislaw The Institute of Computer Science Faculty of Electronics and Information Technology Warsaw University of Technology Warsaw Poland

Building a good heuristics for a computer program for Go is difficult. Game tree is highly branched and there is a threat that the heuristics would eliminate strong moves. Human players often use patterns to decide where to put stones. therefore, one of the ideas is to develop the heuristics based on the database of "good" moves denoted by patterns. A pattern is a small segment of the board. Each pattern’s point can be vacant, occupied by black or white stone or can be an off-board point. A potential move is executed in the center of the pattern. patterns can be acquired from a human expert or through machine learning. this paper presents a technique for: (1) retrieving patterns from a collection of records of games played between human expert players, (2) storing patterns, (3) implementing patterns in a computer program for Go. © Springer international Publishing Switzerland 2015.

关键词： Artificial intelligence

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A novel hybrid CNN-AIS visual pattern recognition engine 6th

A novel hybrid CNN-AIS visual pattern recognition engine

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6th international conference on pattern recognition and machine intelligence, premi 2015

作者： Bhalla, Vandna Chaudhury, Santanu Jain, Arihant Indian Institute of Technology Delhi New Delhi India

machine learning methods are used today mostly for recognition problems. Convolutional Neural Networks (CNN) have time and again proved successful for many image processing tasks primarily for their architecture. In this paper we propose to apply CNN to small data sets like for example, personal photo albums or other similar environs where the size of training dataset is a limitation, within the framework of a proposed hybrid CNN-AIS model. We use Artificial Immune System Principles to enhance the small size of training data set. A layer of Clonal Selection is added to the local filtering and max pooling of CNN Architecture. the proposed Architecture is evaluated using the standard MNIST dataset by limiting the data size and also with a small personal data sample belonging to two different classes. Experimental results show that the proposed hybrid CNN-AIS based recognition engine works well when the size of training data is limited in size. © Springer international Publishing Switzerland 2015.

关键词： Neural networks

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Bayesian Approach to the pattern recognition Problem in Nonstationary Environment

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4th international conference on pattern recognition and machine intelligence (premi)

作者： Krasotkina, O. V. Mottl, V. V. Turkov, P. A. Tula State Univ Tula Russia RAS Comp Ctr Moscow Russia

ISBN: (纸本)9783642217869

the classical learning problem of the pattern recognition in a finite-dimensional linear space of real-valued features is studied under the conditions of a non-stationary universe. the training criterion of non-stationary pattern recognition is formulated as a generalization of the classical Support Vector machine. the respective numerical algorithm has the computation complexity proportional to the length of the training time series.

关键词： pattern recognition

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Simplifying contextual structures 6th

Simplifying contextual structures

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6th international conference on pattern recognition and machine intelligence, premi 2015

作者： Düntsch, Ivo Gediga, Günther Brock University St. CatharinesONL2S 3A1 Canada Department of Psychology Institut IV Universität Münster Fliednerstr. 21 Münster Germany

We present a method to simplify a formal context while retaining much of its information content. Although simple, our ICRA approach offers an effective way to reduce the complexity of a concept lattice and/or a knowledge space by changing only little information in comparison to a competing model which uses fuzzy K-Means clustering. © Springer international Publishing Switzerland 2015.

关键词： Artificial intelligence

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Combining ensembles algorithms of symbolic learners 6

Combining ensembles algorithms of symbolic learners

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6th international conference on Information, intelligence, Systems and Applications (IISA)

作者： Lipitakis, Anastasia-Dimitra Kotsiantis, Sotiris Univ Patras Dept Math Patras Greece

ISBN: (纸本)9781467393119

In this research work an ensemble of bagging, boosting, rotation forest, decorate and random subspace methods with 5 symbolic sub-classifiers in each one is presented. then a voting methodology is used for the final prediction. In order to decrease training time, before building the ensemble redundant features were removed using a slight filter feature selection method. A comparison with simple bagging, boosting, rotation forest, decorate and random subspace methods ensembles with 25 symbolic sub-classifiers is performed, as well as other well-known combining methods, on standard benchmark datasets. the proposed technique is shown to be more accurate than other related methods in most cases.

关键词： data mining machine learning pattern recognition ensembles of classifiers symbolic learner bagging boosting random subspace methods

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An optimal greedy approximate nearest neighbor method in statistical pattern recognition 6th

An optimal greedy approximate nearest neighbor method in sta...

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6th international conference on pattern recognition and machine intelligence, premi 2015

作者： Savchenko, Andrey V. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

the insufficient performance of statistical recognition of composite objects (images, speech signals) is explored in case of medium-sized database (thousands of classes). In contrast to heuristic approximate nearest-neighbor methods we propose a statistically optimal greedy algorithm. the decision is made based on the Kullback-Leibler minimum information discrimination principle. the model object to be checked at the next step is selected from the class with the maximal likelihood (joint density) of distances to previously checked models. Experimental study results in face recognition task with FERET dataset are presented. It is shown that the proposed method is much more effective than the brute force and fast approximate nearest neighbor algorithms, such as randomized kd-tree, perm-sort, directed enumeration method. © Springer international Publishing Switzerland 2015.

关键词： Face recognition

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引用

6th international conference on pattern recognition and machine intelligence, premi 2015

作者： Maji, Pradipta Shah, Ekta Paul, Sushmita Biomedical Imaging and Bioinformatics Lab Machine Intelligence Unit Indian Statistical Institute Kolkata India Laboratory of Systems Tumor Immunology Department of Dermatology University of Erlangen-Nurnberg Erlangen Germany

One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new similarity measure to compute the functional similarity between two genes. It is based on the information of protein-protein interaction networks. A new gene selection algorithm is introduced to identify disease genes, integrating judiciously the information of gene expression profiles and protein-protein interaction networks. the proposed algorithm selects a set of genes from microarray data as disease genes by maximizing the relevance and functional similarity of the selected genes. the performance of the proposed algorithm, along with a comparison with other related methods, is demonstrated on colorectal cancer data set. © Springer international Publishing Switzerland 2015.

关键词： Proteins

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