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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3281 条记录，以下是2421-2430 订阅

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An Unsupervised Emotional Scene Retrieval Framework for Lifelog Videos 3

An Unsupervised Emotional Scene Retrieval Framework for Life...

引用

3rd IIAI international conference on Advanced Applied Informatics (IIAI-AAI)

作者： Nomiya, Hiroki Morikuni, Atsushi Hochin, Teruhisa Kyoto Inst Technol Sakyo Ku Kyoto 6068585 Japan

ISBN: (纸本)9781479941735

In order to promote the utilization of lifelog videos, an effective retrieval framework of the emotional scenes, which are considered to be important scenes, is proposed in this paper. The proposed method is based on facial expression recognition since the emotional scenes can be detected by taking the facial expressions into consideration. Most of conventional facial expression recognition methods require a large amount of training data to construct a recognition model. Adopting such methods for large-scale video databases is unrealistic because preparing sufficient training data requires considerable human efforts. We introduce an unsupervised machine learning framework to solve this issue by making it possible to construct a facial expression recognition model without any training data set. The proposed method is evaluated through an emotional scene detection experiment. A prototype of the emotional scene retrieval system based on the proposed emotional scene detection method is implemented.

关键词： Lifelog video retrieval facial expression recognition clustering ensemble learning

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Defensibility-based Classification for Argument mining 14

Defensibility-based Classification for Argument Mining

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14th IEEE international conference on data mining (IEEE ICDM)

作者： Kido, Hiroyuki Ohsawa, Yukio Univ Tokyo Sch Engn Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

ISBN: (纸本)9781479942749

This paper shows a preliminary report regarding classification techniques based on argumentation theory in artificial intelligence. A classification problem is defined on a directed graph, i.e., an argumentation framework, where each node represents an argument and each edge an attack relation between connected arguments. A hypothesis space is defined by all possible argumentation consequences, i.e., extensions. A target argument is classified as justified or overruled, according to the best extensions minimizing errors with respect to training examples, i.e.,tuples of arguments and their correct classes. We give ideal downward and upward refinement operators for calculating hypotheses step by step. Algorithm analysis and performance evaluation are future work.

关键词： data mining directed graphs heuristic programming pattern classification argument mining argumentation consequences argumentation theory artificial intelligence defensibility-based classification directed graph downward refinement operators hypothesis space upward refinement operators conferences Cost function data mining Lattices machine learning algorithms Semantics Training directed graphs data mining Heuristic programming machine learning algorithms Artificial Intelligence Cost functions pattern recognition crystal lattices Semantics Disputes Functional training

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mining the Big data: The Critical Feature Dimension Problem 3

Mining the Big Data: The Critical Feature Dimension Problem

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3rd IIAI international conference on Advanced Applied Informatics (IIAI-AAI)

作者： Liu, Qingzhong Sung, Andrew H. Ribeiro, Bernardete Suryakumar, Divya Sam Houston State Univ Dept Comp Sci Huntsville TX 77341 USA Univ So Mississippi Sch Comp Hattiesburg MS 39406 USA Univ Coimbra Dept Informat Engn P-3030290 Coimbra Portugal Wipro Technol Ltd Appl Machine Learning Mountain View CA 94043 USA

ISBN: (纸本)9781479941735

In mining massive datasets, often two of the most important and immediate problems are sampling and feature selection. Proper sampling and feature selection contributes to reducing the size of the dataset while obtaining satisfactory results in model building. Theoretically, therefore, it is interesting to investigate whether a given dataset possesses a critical feature dimension, or the minimum number of features that is required for a given learning machine to achieve "satisfactory" performance. (Likewise, the critical sampling size problem concerns whether, for a given dataset, there is a minimum number of data points that must be included in any sample for a learning machine to achieve satisfactory performance.) Here the specific meaning of "satisfactory" performance is to be defined by the user. This paper addresses the complexity of both problems in one general theoretical setting and shows that they have the same complexity and are highly intractable. Next, an empirical method is applied in an attempt to find the approximate critical feature dimension of datasets. It is demonstrated that, under generally reasonable assumptions pertaining to feature ranking algorithms, the critical feature dimension are successfully discovered by the empirical method for a number of datasets of various sizes. The results are encouraging in achieving significant feature size reduction and point to a promising way in dealing with big data. The significance of the existence of crucial dimension in datasets is also explained.

关键词： data mining machine learning feature ranking dimension reduction critical dimension

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Applying machine learning techniques to baseball pitch prediction

Applying machine learning techniques to baseball pitch predi...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Hamilton, Michael Hoang, Phuong Layne, Lori Murray, Joseph Padgett, David Stafford, Corey Tran, Hien Mathematics Department Rutgers University New Brunswick NJ United States Department of Mathematics North Carolina State University Raleigh NC United States MIT Lincoln Laboratory Lexington MA United States Department of Applied Physics and Applied Mathematics Columbia University New York NY United States

ISBN: (纸本)9789897580185

Major League Baseball, a professional baseball league in the US and Canada, is one of the most popular sports leagues in North America. Partially because of its popularity and the wide availability of data from games, baseball has become the subject of significant statistical and mathematical analysis. Pitch analysis is especially useful for helping a team better understand the pitch behavior it may face during a game, allowing the team to develop a corresponding batting strategy to combat the predicted pitch behavior. We apply several common machine learning classification methods to PITCH f/x data to classify pitches by type. We then extend the classification task to prediction by utilizing features only known before a pitch is thrown. By performing significant feature analysis and introducing a novel approach for feature selection, moderate improvement over former results is achieved. Copyright © 2014 SCITEPRESS.

关键词： Forecasting

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Enhanced routing algorithm for opportunistic networking: On the improvement of the basic opportunistic networking routing algorithm by the application of machine learning

Enhanced routing algorithm for opportunistic networking: On ...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Janků, Ladislava Smítková Hyniová, Kateřina Institute of Department of Digital Design Faculty of Information Technology Czech Technical University in Prague Thákurova 9 160 00 Praha 6 Czech Republic

ISBN: (纸本)9789897580185

The opportunistic communication networks are special communication networks where no assumption is made on the existence of a complete path between two nodes wishing to communicate;the source and destination nodes needn't be connected to the same network at the same time. This assumption makes the routing in these networks extremely difficult. We proposed the novel opportunistic networking routing algorithm, which improves the basic opportunistic networking routing algorithm by application of machine learning. The HMM Autonomous Robot Mobility Models and Node Reachability Model are constructed from the observed data and used in a proposed routing scheme in order to compute the combined probabilities of message delivery to the destination node. In the proposed routing scheme, the messages are coppied between two nodes only if the combined probability of the message delivery to the destination node is higher than the preliminary defined limit value. The routing scheme was developed for the networks of autonomous mobile robots. The improvement about 70% in a network load is reported. Copyright © 2014 SCITEPRESS.

关键词： Mobile robots

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Manifold learning in data mining Tasks

Manifold Learning in Data Mining Tasks

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10th international conference on machine learning and data mining (MLDM)

作者： Kuleshov, Alexander Bernstein, Alexander Kharkevich Inst Informat Transmiss Problems RAS I Moscow Russia

ISBN: (纸本)9783319089799;9783319089782

Many data mining tasks deal with data which are presented in high dimensional spaces, and the 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving these tasks. To avoid these phenomena, various Representation learning algorithms are used as a first key step in solutions of these tasks to transform the original high-dimensional data into their lower-dimensional representations so that as much information about the original data required for the considered data mining task is preserved as possible. The above Representation learning problems are formulated as various Dimensionality Reduction problems (Sample Embedding, data Manifold embedding, Manifold learning and newly proposed Tangent Bundle Manifold learning) which are motivated by various data mining tasks. A new geometrically motivated algorithm that solves the Tangent Bundle Manifold learning and gives new solutions for all the considered Dimensionality Reduction problems is presented.

关键词： data mining Statistical learning Representation learning Dimensionality Reduction Manifold learning Tangent learning Tangent Bundle Manifold learning

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Speeding up support vector machines: Probabilistic versus nearest neighbour methods for condensing training data

Speeding up support vector machines: Probabilistic versus ne...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Gamboni, Moïri Garg, Abhijai Grishin, Oleg Oh, Seung Man Sowani, Francis Spalvieri-Kruse, Anthony Toussaint, Godfried T. Zhang, Lingliang Faculty of Science New York University Abu Dhabi P.O. Box 129188 Abu Dhabi United Arab Emirates

ISBN: (纸本)9789897580185

Several methods for reducing the running time of support vector machines (SVMs) are compared in terms of speed-up factor and classification accuracy using seven large real world datasets obtained from the UCI machine learning Repository. All the methods tested are based on reducing the size of the training data that is then fed to the SVM. Two probabilistic methods are investigated that run in linear time with respect to the size of the training data: blind random sampling and a new method for guided random sampling (Gaussian Condensing). These methods are compared with k-Nearest Neighbour methods for reducing the size of the training set and for smoothing the decision boundary. For all the datasets tested blind random sampling gave the best results for speeding up SVMs without significantly sacrificing classification accuracy. Copyright © 2014 SCITEPRESS.

关键词： Support vector machines

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ICPRAM 2014 - Proceedings of the 3rd international conference on pattern recognition Applications and Methods

ICPRAM 2014 - Proceedings of the 3rd International Conferenc...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

ISBN: (纸本)9789897580185

The proceedings contain 104 papers. The topics discussed include: multiple segmentation of image stacks;measuring cluster similarity by the travel time between data points;affine invariant shape matching using histogram of radon transform and angle correlation matrix;unsupervised consensus functions applied to ensemble biclustering;modified fuzzy C-means as a stereo segmentation method;on selecting helpful unlabeled data for improving semi-supervised support vector machines;SCHOG feature for pedestrian detection;discriminative prior bias learning for pattern classification;minutiae persistence among multiple samples of the same person's fingerprint in a cooperative user scenario;dirichlet-tree distribution enhanced random forests for head pose estimation;and a descriptor based on intensity binning for image matching.

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Finding Multi-dimensional patterns in Healthcare

Finding Multi-dimensional Patterns in Healthcare

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10th international conference on machine learning and data mining (MLDM)

作者： Silva, Andreia Antunes, Claudia Univ Lisbon Inst Super Tecn Dept Comp Sci & Engn P-1699 Lisbon Portugal

ISBN: (纸本)9783319089799;9783319089782

The amount of healthcare data is increasing at a rapid pace, and with that is also increasing the need for better and automated analyzes that are able to transform these data into useful knowledge. In turn, this knowledge may bring huge benefits to the healthcare management and leverage the healthcare system in all aspects. In the last decade, data mining has been widely applied to this domain and several approaches, technology and methods were developed for improving decision support. Despite the advances in healthcare mining, the characteristics of these data - complexity, volume, high dimensionality, etc. - still demand more efficient and effective techniques. In this work we present a case study on the healthcare domain. We propose to use a multi-dimensional model of the hepatitis dataset and to apply a multi-dimensional data mining algorithm to find all patterns in the database. These patterns are then used to enrich classification and results show a significant improvement on prediction.

关键词： pattern mining High Dimensionality Healthcare Domain Classification

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Investigating Long Short-Term Memory Networks for Various pattern recognition Problems

Investigating Long Short-Term Memory Networks for Various Pa...

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10th international conference on machine learning and data mining (MLDM)

作者： Otte, Sebastian Liwicki, Marcus Krechel, Dirk Univ Appl Sci Wiesbaden Wiesbaden Germany German Res Ctr Kaiserslautern Germany

ISBN: (纸本)9783319089799;9783319089782

The purpose of this paper is to further investigate how and why long short-term memory networks (LSTM) perform so well on several pattern recognition problems. Our contribution is three-fold. First, we describe the main highlights of the LSTM architecture, especially when compared to standard recurrent neural networks (SRN). Second, we give an overview of previous studies to analyze the behavior of LSTMs on toy problems and some realistic data in the speech recognition domain. Third, the behavior of LSTMs is analyzed on novel problems which are relevant for pattern recognition research. Thereby, we analyze the ability of LSTMs to classify long sequences containing specific patterns at an arbitrary position on iteratively increasing the complexity of the problem under constant training conditions. We also compare the behavior of LSTMs to SRNs for text vs. non-text sequence classification on a real-world problem with significant non-local time-dependencies where the features are computed only locally. Finally, we discuss why LSTMs with standard training methods are not suited for the task of signature verification.

关键词： Long short-term memory

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