Scheduled Maintenance on March 25th, 2017:
Single article purchases and IEEE account management will be unavailable from 4:00 AM until 6:30 PM (ET). We apologize for the inconvenience.
By Topic

2010 Ninth International Conference on Machine Learning and Applications

12-14 Dec. 2010

Filter Results

Displaying Results 1 - 25 of 171
  • [Front cover]

    Publication Year: 2010, Page(s): C1
    Request permission for commercial reuse | PDF file iconPDF (252 KB)
    Freely Available from IEEE
  • [Title page i]

    Publication Year: 2010, Page(s): i
    Request permission for commercial reuse | PDF file iconPDF (8 KB)
    Freely Available from IEEE
  • [Title page iii]

    Publication Year: 2010, Page(s): iii
    Request permission for commercial reuse | PDF file iconPDF (280 KB)
    Freely Available from IEEE
  • [Copyright notice]

    Publication Year: 2010, Page(s): iv
    Request permission for commercial reuse | PDF file iconPDF (108 KB)
    Freely Available from IEEE
  • Table of contents

    Publication Year: 2010, Page(s):v - xvi
    Request permission for commercial reuse | PDF file iconPDF (206 KB)
    Freely Available from IEEE
  • Preface

    Publication Year: 2010, Page(s): xvii
    Request permission for commercial reuse | PDF file iconPDF (71 KB) | HTML iconHTML
    Freely Available from IEEE
  • Conference Committees and Reviewers

    Publication Year: 2010, Page(s):xviii - xxiii
    Request permission for commercial reuse | PDF file iconPDF (101 KB)
    Freely Available from IEEE
  • Keynote Talks

    Publication Year: 2010, Page(s):xxiv - xxvii
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (79 KB)

    We describe research to develop a never-ending language learner that runs 24 hours per day, forever, and that each day has two goals. The first is to extract more information from the web to populate its growing knowledge base of structured knowledge. The second is to learn to read better than yesterday, as evidenced by its ability to go back to the same web pages it read yesterday, and extract mo... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Improved Fine-Grained Component-Conditional Class Labeling with Active Learning

    Publication Year: 2010, Page(s):3 - 8
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1015 KB) | HTML iconHTML

    We have recently introduced new generative semi supervised mixtures with more fine-grained class label generation mechanisms than previous methods. Our models combine advantages of semi supervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest-prototype (NP) classification, which achieves accurate classification in the vicinity of labeled samples. O... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Novel Noise Filtering Algorithm for Imbalanced Data

    Publication Year: 2010, Page(s):9 - 14
    Cited by:  Papers (4)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (194 KB) | HTML iconHTML

    Noise filtering is a commonly-used methodology to improve the performance of learners built using low-quality data. A common type of noise filtering is a data preprocessing technique called classification filtering. In classification filtering, a classifier is built and evaluated on the training dataset (typically using cross-validation) and any misclassified instances are considered noisy. The st... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Dynamic Batch Size Selection for Batch Mode Active Learning in Biometrics

    Publication Year: 2010, Page(s):15 - 22
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (958 KB) | HTML iconHTML

    Robust biometric recognition is of paramount importance in security and surveillance applications. In face based biometric systems, data is usually collected using a video camera with high frame rate and thus the captured data has high redundancy. Selecting the appropriate instances from this data to update a classification model, is a significant, yet valuable challenge. Active learning methods h... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Study of Smoothing Algorithms for Item Categorization on e-Commerce Sites

    Publication Year: 2010, Page(s):23 - 28
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (188 KB) | HTML iconHTML

    One central issue in a long-tail online marketplace such as eBay is to automatically put user self-input items into a catalog in real time. This task is extremely challenging when the inventory scales up, the items become ephemeral, and the user input remains noisy. Indeed, catalog learning has emerged as a key technical property for other major online ecommerce applications including search and r... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • From Serve-on-Demand to Serve-on-Need: A Game Theoretic Approach

    Publication Year: 2010, Page(s):31 - 36
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (373 KB) | HTML iconHTML

    Everyone is familiar with the scenario, people demand or assign tasks to robots, and robots execute the tasks to serve people. We call such a model Serve-on-Demand. With the advancement of pervasive computing, machine learning and artificial intelligence, the robot service of the next generation will inevitably turn to actively and exactly meet people's needs, even without explicit demand. We call... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Autonomous Navigation in Dynamic Environments with Reinforcement Learning and Heuristic

    Publication Year: 2010, Page(s):37 - 42
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (482 KB) | HTML iconHTML

    Researchers have created machines which operate autonomously in complex and changing environments. An important problem that has been widely studied is that of autonomous navigation systems, through which attempts have been made to create mechanisms with their own decision making in complex environments. Ideally, an autonomous navigation agent must have an ability to learn while working in its env... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Public Goods Game Simulator with Reinforcement Learning Agents

    Publication Year: 2010, Page(s):43 - 49
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (340 KB) | HTML iconHTML

    As a famous game in the domain of game theory, both pervasive empirical studies as well as intensive theoretical analysis have been conducted and performed worldwide to research different public goods game scenarios. At the same time, computer game simulators are utilized widely for better research of game theory by providing easy but powerful visualization and statistics functionalities. However,... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • MMM-PHC: A Particle-Based Multi-Agent Learning Algorithm

    Publication Year: 2010, Page(s):50 - 55
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (478 KB) | HTML iconHTML

    Learning is one way to determine how agents should act, but learning in multi-agent systems is more difficult than in single-agent systems because other learning agents modify their behavior. We introduce a particle-based algorithm called MMM-PHC. MMM-PHC promotes convergence to Nash equilibria in matrix games using the ideas of maxim in strategies and partial commitment. Partial commitment is imp... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An All-at-once Unimodal SVM Approach for Ordinal Classification

    Publication Year: 2010, Page(s):59 - 64
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (586 KB) | HTML iconHTML

    Support vector machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since then, little work has been done for the case where the classes are ordered. Usually one constructs a nominal classifier and a posteriori defines the order. The definition of an ordinal classifier leads to a better generalisation. ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Centroid-based Classification Enhanced with Wikipedia

    Publication Year: 2010, Page(s):65 - 70
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (316 KB) | HTML iconHTML

    Most of the traditional text classification methods employ Bag of Words (BOW) approaches relying on the words frequencies existing within the training corpus and the testing documents. Recently, studies have examined using external knowledge to enrich the text representation of documents. Some have focused on using WordNet which suffers from different limitations including the available number of ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Classification Models with Global Constraints for Ordinal Data

    Publication Year: 2010, Page(s):71 - 77
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (231 KB) | HTML iconHTML

    Ordinal classification is a form of multi-class classification where there is an inherent ordering between the classes, but not a meaningful numeric difference between them. Although conventional methods, designed for nominal classes or regression problems, can be used to solve the ordinal data problem, there are benefits in developing models specific to this kind of data. This paper introduces a ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Appearance Based Recognition Using Spatial and Discriminant Influence

    Publication Year: 2010, Page(s):78 - 83
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (510 KB) | HTML iconHTML

    Appearances of objects lie in high-dimensional spaces. For a given recognition task, feature selection aims to select most effective features in order to reduce the recognition cost and improve recognition accuracy. Feature selection can be achieved by a bottom-up scheme, e.g., using spatial information, or a top-down scheme, e.g., using class information. In this paper, we propose a model to inte... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multi-Class Classification Using a New Sigmoid Loss Function for Minimum Classification Error (MCE)

    Publication Year: 2010, Page(s):84 - 89
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (289 KB) | HTML iconHTML

    A new loss function has been introduced for Minimum Classification Error, that approaches optimal Bayes' risk and also gives an improvement in performance over standard MCE systems when evaluated on the Aurora connected digits database. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Multimodel Approach of Complex Systems Identification and Control Using Neural and Fuzzy Clustering Algorithms

    Publication Year: 2010, Page(s):93 - 98
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (285 KB) | HTML iconHTML

    This paper deals with a new approach for complex systems modeling and control based on neural and fuzzy clustering algorithms. It aims to derive a base of local models describing the system in the whole operating domain. The implementation of this approach requires three main steps: 1) determination of the structure of the model-base, the number of models are found out by using Rival Penalized Com... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Learning Collaborative Behavior by Observation

    Publication Year: 2010, Page(s):99 - 104
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (458 KB) | HTML iconHTML

    This paper presents a multi-agent framework capable of learning teamwork by observation. The system combines proven single entity learning by observation techniques with a multi-agent system shown to exhibit effective teamwork. An effective simulated production team is observed. An off-line training algorithm uses the observed data to develop behavior maps for a Collaborative Context-based Reasoni... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Heterogeneous Imitation Learning from Demonstrators of Varying Physiology and Skill

    Publication Year: 2010, Page(s):105 - 112
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1070 KB) | HTML iconHTML

    Imitation learning enables a learner to improve its abilities by observing others. Most robotic imitation learning systems only learn from demonstrators that are homogeneous physiologically (i.e. the same size and mode of locomotion) and in terms of skill level. To successfully learn from physically heterogeneous robots that may also vary in ability, the imitator must be able to abstract behaviour... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multimodal Parameter-exploring Policy Gradients

    Publication Year: 2010, Page(s):113 - 118
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1141 KB) | HTML iconHTML

    Policy Gradients with Parameter-based Exploration (PGPE) is a novel model-free reinforcement learning method that alleviates the problem of high-variance gradient estimates encountered in normal policy gradient methods. It has been shown to drastically speed up convergence for several large-scale reinforcement learning tasks. However the independent normal distributions used by PGPE to search thro... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.