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检索条件"任意字段=5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004"

共 182 条记录，以下是81-90 订阅

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Machine learning or discrete choice models for car ownership demand estimation and prediction? 5

Machine learning or discrete choice models for car ownership...

引用

5th IEEE international conference on Models and Technologies for intelligent Transportation Systems, MT-ITS 2017

作者： Paredes, Miguel Hemberg, Erik O'Reilly, Una-May Zegras, Chris CSAIL and DUSP Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781509064847

Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. they show how some distinct choice of human behavior or preference influences a decision. they are also used to project future demand estimates to support policy exploration. this latter use for prediction is indirectly aligned with and conditional to the model's estimation which aims to fit the observed data. In contrast, machine learning models are derived to maximize prediction accuracy through mechanisms such as out-of-sample validation, non-linear structure, and automated covariate selection, albeit at the expense of interpretability and sound behavioral theory. We investigate how machine learning models can outperform discrete choice models for prediction of car ownership using transportation household survey data from Singapore. We compare our household car ownership model (multinomial logit model) against various machine learning models (e.g. Random Forest, Support Vector Machines) by using 2008 data to derive, i.e. estimate models that we then use to predict 2012 ownership. the machine learning models are inferior to the discrete choice model when using discrete choice features. However, after engineering features more appropriate for machine learning they are superior. these results highlight both the cost of applying machine learning models in econometric contexts and an opportunity for improved prediction and better urban policy making through machine learning models with appropriate features. © 2017 IEEE.

关键词： Forecasting

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Sparse Non-negative Matrix Factorization with Generalized Kullback-Leibler Divergence 17th

Sparse Non-negative Matrix Factorization with Generalized Ku...

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Chen, Jingwei Feng, Yong Liu, Yang Tang, Bing Wu, Wenyuan Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Automated Reasoning & Cognit Chongqing 400714 Peoples R China Chongqing Jiaotong Univ Coll Informat Sci & Engn Chongqing 400074 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China

ISBN: (纸本)9783319462578;9783319462561

Non-negative Matrix Factorization (NMF), especially with sparseness constraints, plays a critically important role in data engineering and machine learning. Hoyer (2004) presented an algorithm to compute NMF with exact sparseness constraints. the exact sparseness constraints depends on a projection operator. In the present work, we first give a very simple counterexample, for which the projection operator of the Hoyer (2004) algorithm fails. After analysing the reason geometrically, we fix this bug by adding some random terms and show that the fixed one works correctly. Based on the fixed projection operator, we propose another sparse NMF algorithm aiming at optimizing the generalized Kullback-Leibler divergence, hence named SNMF-GKLD. Experimental results show that SNMF-GKLD not only has similar effects with Hoyer (2004) on the same data sets, but is also efficient.

关键词： Non-negative Matrix Factorization Projection operator Generalized Kullback-Leibler divergence

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Freight Vehicle Travel Time Prediction Using Sparse Gaussian Processes Regression with Trajectory data 1

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Li, Xia Bai, Ruibin Univ Nottingham Ningbo China Ningbo 315100 Zhejiang Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. the results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. the overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. the computational complexity of SGPR models is O(mn(2)), and it does not require lengthy model fitting process as SVR and ANN. this makes GP models more practicable for real-world practice in large-scale transportation data analyses.

关键词： Travel time prediction Machine learning Gaussian Processes Sparse approximation Trajectory data Freight vehicle

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Using the 5th dimensions of human visual perception to inspire automated edge and texture segmentation: A fuzzy spatial-taxon approach

Using the 5th dimensions of human visual perception to inspi...

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IEEE international conference on Cognitive Informatics

作者： Lauren Barghout Visiting Scholar at Berkeley Initiative for Soft Computing Electrical Engineering and Computer Science University of California at Berkeley Berkeley CA USA

ISBN: (纸本)9781509038473

With the recent stunning success of machine learning, artificially intelligent machine vision research falls (roughly) into two camps: the big data camp and cognitive informatics camp. Big data uses statistical methods to discover latent structures that emerge from the co-occurrences of relevant features when sampling over enormous quantities of data. the cognitive informatics methods design computer vision systems to mimic human cognition. though some visual latent features that emerge from deep learning networks, mimic mammalian visual detectors, as of yet the information processing mechanisms (analogous to human psychophysical mechanisms) remain hidden within the complexity of the deep nets. Furthermore, the sampling requirements of big data systems require limiting samples to pre-processed sets, such as SHIFT (shift invariant feature transform (Lowe 1999)). Techniques, such as the ones introduced in this paper, provide fast cognitively relevant methods for selecting samples and reducing the number of candidate features. the approach described in this paper live squarely in the camp of designing computer vision A.I. to mimics human cognitive processes. I introduce a novel definition of edges, based on human hierarchical scene perception. Hierarchical scene perception views vision within the 5 dimensions of horizontal & vertical position, depth, time and scene abstraction level (spatial-taxon). Fuzzy inference selects candidate edge elements using the Gestalt psychology principal of good curvilinear continuation, proximity and edges attachment. Spatial-taxon inference infers an edge outline for each level of abstraction within the scene architecture. the system was tested on 60 natural images and the results provide edges more aligned with human intuition of what edges should look like. ROC plots indicate solid performance, with the majority of human subjects rating the edge detection as high quality. the inferred edges are consistent with the finding of neurons r

关键词： Image edge detection Computer vision Laplace equations Big data Image segmentation Cognitive informatics Visualization

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Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Saez, Jose A. Luengo, Julian Stefanowski, Jerzy Herrera, Francisco Univ Granada CITIC UGR Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Burgos Dept Civil Engn LSI E-09006 Burgos Spain Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland

ISBN: (纸本)9783319108407;9783319108391

Imbalance data constitutes a great difficulty for most algorithms learning classifiers. However, as recent works claim, class imbalance is not a problem in itself and performance degradation is also associated with other factors related to the distribution of the data as the presence of noisy and borderline examples in the areas surrounding class boundaries. this contribution proposes to extend SMOTE with a noise filter called Iterative-Partitioning Filter (IPF), which can overcome these problems. the properties of this proposal are discussed in a controlled experimental study against SMOTE and its most well-known generalizations. the results show that the new proposal performs better than exiting SMOTE generalizations for all these different scenarios.

关键词： Classification imbalanced data SMOTE class noise noise filters

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Fast Frequent Pattern Detection Using Prime Numbers

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Xylogiannopoulos, Konstantinos F. Addam, Omar Karampelas, Panagiotis Alhajj, Reda Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada Hellen Amer Univ Dept Informat Technol Manchester NH USA Global Univ Dept Comp Sci Beirut Lebanon Hellen Air Force Acad Dept Informat Computers Attica Greece

ISBN: (纸本)9783319108407;9783319108391

Finding all frequent itemsets (patterns) in a given database is a challenging process that in general consumes time and space. Time is measured in terms of the number of database scans required to produce all frequent itemsets. Space is consumed by the number of potential frequent itemsets which will end up classified as not frequent. To overcome both limitations, namely space and time, we propose a novel approach for generating all possible frequent itemsets by introducing a new representation of items into groups of four items and within each group, items are assigned one of four prime numbers, namely 2, 3, 5, and 7. the reported results demonstrate the applicability and effectiveness of the proposed approach. Our approach satisfies scalability in terms of number of transactions and number of items.

关键词： frequent pattern mining association rules data mining prime representation

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Deep learning on Natural Viewing Behaviors to Differentiate Children with Fetal Alcohol Spectrum Disorder

Deep Learning on Natural Viewing Behaviors to Differentiate ...

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14th international conference on intelligent data engineering and automated learning (ideal)

作者： Tseng, Po-He Paolozza, Angelina Munoz, Douglas P. Reynolds, James N. Itti, Laurent Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Queens Univ Ctr Neurosci Studies Kingston ON K7L 3N6 Canada

ISBN: (纸本)9783642412783;9783642412776

Computational models of visual attention have attracted strong interest by accurately predicting how humans deploy attention. However, little research has utilized these models to detect clinical populations whose attention control has been affected by neurological disorders. We designed a framework to decypher disorders from the joint analysis of video and patients' natural eye movement behaviors (watch television for 5 minutes). We employ convolutional deep neural networks to extract visual features in real-time at the point of gaze, followed by SVM and Adaboost to classify typically developing children vs. children with fetal alcohol spectrum disorder (FASD), who exhibit impaired attentional control. the classifier achieved 74.1% accuracy (ROC: 0.82). Our results demonstrate that there is substantial information about attentional control in even very short recordings of natural viewing behavior. Our new method could lead to high-throughput, low-cost screening tools for identifying individuals with deficits in attentional control.

关键词： deep learning sparse representation eye movement natural scenes clinical populations

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Advanced CT and MR image processing with FPGA

Advanced CT and MR image processing with FPGA

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13th international conference on intelligent data engineering and automated learning, ideal 2012

作者： Kasik, Vladimir Cerny, Martin Penhaker, Marek Snášel, Václav Novak, Vilem Pustkova, Radka VSB - Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical Engineering 17. listopadu 15 Ostrava 70833 Czech Republic Faculty Hospital Ostrava Child Neurology Clinic 17. listopadu 1790/5 Ostrava Czech Republic

ISBN: (纸本)9783642326387

the CT and MRI image processing is a task with high requirement of computation time and computation speed. the calculation process in the most cases consists of various steps that are inherently parallel. there is opportunity to use parallel programming implemented in standard computer with more than one core. the more efficient way is to use real parallel signal processing. the only hardware that allows parallel data processing is FPGA. this article deals about this possibility. It is shown in the application on real problem in the image processing field. the first there is described the methodology of image processing in the application on CT (MRI) images of the head. the task is to count the ratio of intracranial fluid in the skull. It is important for child neurology diagnostic. the second part deals about the FPGA and its contribution to solving this task in the real parallel hardware system. © 2012 Springer-Verlag.

关键词： Field programmable gate arrays (FPGA)

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Investigating the Behaviour of Radial Basis Function Networks in Regression and Classification of Geospatial data

Investigating the Behaviour of Radial Basis Function Network...

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11th international conference on intelligent data engineering and automated learning

作者： Guidali, Andrea Binaghi, Elisabetta Guglielmin, Mauro Pascale, Marco Univ Insubria Dept Comp Sci & Commun Via Mazzini 5 I-21100 Varese Italy Univ Insubria Dept Structural & Funct Biol I-21100 Varese Italy

ISBN: (纸本)9783642153808

this work investigates learning and generalisation capabilities of Radial Basis Function Networks used to solve function regression and classification tasks in the environmental context. In particular RBFN is applied to solve the problem of snow cover thickness estimation in which critical aspects such as minimal training condition, weak pattern description and inconsistency among data arise. the RBFN shows good performances and high flexibility in coping with regression, hard and soft classifications which are complementary tasks in the analysis of complex environmental phenomena.

关键词： Radial Basis Function Networks Regression Classification Fuzzy sets Geospatial data

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Generalized Derivative Based Kernelized learning Vector Quantization

Generalized Derivative Based Kernelized Learning Vector Quan...

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11th international conference on intelligent data engineering and automated learning

作者： Schleif, Frank-Michael Villmann, thomas Hammer, Barbara Schneider, Petra Biehl, Michael Univ Bielefeld Dept Techn Univ Str 21-23 D-33615 Bielefeld Germany Univ Appl Faculty Math Natural & CS D-09648 Mittweida Germany Univ Groningen Johann Bernoulli Inst Math & CS NL-9700 Groningen Netherlands

ISBN: (纸本)9783642153808

We derive a novel derivative based version of kernelized Generalized learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing that D-KGLVQ is competitive with KGLVQ and the SVM on UCI data and additionally show that automatic parameter adaptation for the used kernels simplifies the learning.

关键词： Vector quantization

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