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共 585 条记录，以下是381-390 订阅

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Combining crowd-generated media and personal data: Semi-supervised learning for context recognition

Combining crowd-generated media and personal data: Semi-supe...

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1st ACM international workshop on Personal data Meets Distributed Multimedia, PDM 2013 - Co-located with ACM Multimedia 2013

作者： Nguyen-Dinh, Long-Van Rossi, Mirco Blanke, Ulf Tröster, Gerhard Wearable Computing Lab. ETH Zurich Zurich Switzerland

ISBN: (纸本)9781450323970

The growing ubiquity of sensors in mobile phones has opened many opportunities for personal daily activity sensing. Most context recognition systems require a cumbersome preparation by collecting and manually annotating training examples. Recently, mining online crowd-generated repositories for free annotated training data has been proposed to build context models. A crowd-generated dataset can capture a large variety both in terms of class number and in intra-class diversity, but may not cover all user-specific contexts. Thus, performance is often significantly worse than that of user-centric training. In this work, we exploit for the first time the combination of both crowd-generated audio dataset available in the web and unlabeled audio data obtained from users' mobile phones. We use a semi-supervised Gaussian mixture model to combine labeled data from the crowd-generated database and unlabeled personal recording data. Hereby we refine generic knowledge with data from the user to train a personalized model. This technique has been tested on 7 users on mobile phones with a total data of 14 days and up to 9 context classes. Preliminary results show that a semi-supervised model can improve the recognition accuracy up to 21%. © 2013 ACM.

关键词： Cellular telephones

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New Frontiers in mining Complex patterns - First international workshop, NFMCP 2012, Held in Conjunction with ECML-PKDD 2012, Revised Selected Papers

New Frontiers in Mining Complex Patterns - First Internation...

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1st international workshop on New Frontiers in mining Complex patterns, NFMCP 2012

ISBN: (纸本)9783642373817

The proceedings contain 15 papers. The topics discussed include: learning with configurable operators and RL-based heuristics;reducing examples in relational learning with bounded-treewidth hypotheses;mining complex event patterns in computer networks;learning in the presence of large fluctuations: a study of aggregation and correlation;machine learning as an objective approach to understanding music;pair-based object-driven action rules;effectively grouping trajectory streams;healthcare trajectory mining by combining multidimensional component and itemsets;graph-based approaches to clustering network-constrained trajectory data;finding the most descriptive substructures in graphs with discrete and numeric labels;learning in probabilistic graphs exploiting language-constrained patterns;improving robustness and flexibility of concept taxonomy learning from text;and context-aware predictions on business processes: an ensemble-based solution.

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Fusing traffic sensor data for real-time road conditions

Fusing traffic sensor data for real-time road conditions

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1st international workshop on Sensing and Big data mining, SenseMine 2013

作者： Bouillet, Eric Chen, Bei Cooper, Chris Dahlem, Dominik Verscheure, Olivier IBM Research Ireland Ireland IBM UK United Kingdom

ISBN: (纸本)9781450324304

Transport authorities have been deploying and utilising sensor infrastructures in order to improve upon the level of transport-related services within cities. As existing resources are more and more constrained, novel means of utilising the data originating from these sensors are sought. Advanced IT infrastructures enable real-time processing use cases, such as travel time estimations along defined corridors. One major challenge is to deploy general purpose machine learning algorithms that are able to learn relationships between the covariates and a defined response variable. We introduce a data fusion approach using generalised additive models (GAM) to estimate journey times online in a real-time streaming platform. We experiments with bluetooth sensors and weather information to improve the estimation of journey times along a defined A3 corridor in south-central London. Our approach is able to continuously improve the journey time estimation as new (high-frequency) data becomes available. Our fusion platform also generalises to be able to process more data sources and it scales to city-wide deployments. This way, existing legacy sensor deployments can be utilised for novel value-added services and investments into infrastructural sensor deployments can be assessed in a data-driven way and on a needs basis. © 2013 ACM.

关键词： Big data

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learning with configurable operators and RL-based heuristics

Learning with configurable operators and RL-based heuristics

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1st international workshop on New Frontiers in mining Complex patterns, NFMCP 2012

作者： Martínez-Plumed, Fernando Ferri, Cèsar Hernández-Orallo, José Ramírez-Quintana, María José DSIC Universitat Politècnica de València Camí de Vera s/n 46022 València Spain

ISBN: (纸本)9783642373817

In this paper, we push forward the idea of machine learning systems for which the operators can be modified and finetuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) their operators, according to the problem, data representation and the way the information should be navigated. To achieve this goal, data instances, background knowledge, rules, programs and operators are all written in the same functional language, Erlang. Since changing operators affect how the search space needs to be explored, heuristics are learnt as a result of a decision process based on reinforcement learning where each action is defined as a choice of operator and rule. As a result, the architecture can be seen as a 'system for writing machine learning systems' or to explore new operators. © 2013 Springer-Verlag.

关键词： Reinforcement learning

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Building statistical Language Models of code

Building Statistical Language Models of code

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international workshop on data Analysis patterns in Software Engineering (DAPSE)

作者： Peter Schulam Roni Rosenfeld Premkumar Devanbu Language Technologies Institute Carnegie Mellon University USA Department of Computer Science University of California Davis USA

ISBN: (纸本)9781467362955

We present the Source Code statistical Language Model data analysis pattern. statistical language models have been an enabling tool for a wide array of important language technologies. Speech recognition, machine translation, and document summarization (to name a few) all rely on statistical language models to assign probability estimates to natural language utterances or sentences. In this data analysis pattern, we describe the process of building n-gram language models over software source files. We hope that by introducing the empirical software engineering community to best practices that have been established over the years in research for natural languages, statistical language models can become a tool that SE researchers are able to use to explore new research directions.

关键词： data models Vocabulary Smoothing methods Speech recognition Buildings Software engineering Natural languages

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A comparison of multivariate mutual information estimators for feature selection

A comparison of multivariate mutual information estimators f...

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1st international Conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Doquire, Gauthier Verleysen, Michel Machine Learning Group - ICTEAM Université Catholique de Louvain Place du Levant 3 1348 Louvain-la-Neuve Belgium

ISBN: (纸本)9789898425980

Mutual Information estimation is an important task for many data mining and machine learning applications. In particular, many feature selection algorithms make use of the mutual information criterion and could thus benefit greatly from a reliable way to estimate this criterion. More precisely, the multivariate mutual information (computed between multivariate random variables) can naturally be combined with very popular search procedure such as the greedy forward to build a subset of the most relevant features. Estimating the mutual information (especially through density functions estimations) between high-dimensional variables is however a hard task in practice, due to the limited number of available data points for real-world problems. This paper compares different popular mutual information estimators and shows how a nearest neighbors-based estimator largely outperforms its competitors when used with high-dimensional data.

关键词： data mining

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Online sequential learning based on enhanced extreme learning machine using leftor right pseudo-inverse

Online sequential learning based on enhanced extreme learnin...

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1st international Conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Zong, Weiwei Lan, Yuan Huang, Guang-Bin School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789898425980

The latest development (Huang et al., 2011) has shown that better generalization performance can be obtained for extreme learning machine (ELM) by adding a positive value to the diagonal of HT H or HHT, where H is the hidden layer output matrix. This paper further extends this enhanced ELM to online sequential learning mode. An online sequential learning algorithm is proposed for SLFNs and other regularization networks, consisting of two formulas for two kinds of scenarios: when initial training data is of small scale or large scale. Performance of proposed online sequential learning algorithm is demonstrated through six benchmarking data sets for both regression and multi-class classification problems.

关键词： Benchmarking

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Unexpected challenges in large scale machine learning

Unexpected challenges in large scale machine learning

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1st international workshop on Big data, streams and Heterogeneous Source mining: Algorithms, Systems, Programming Models and Applications, BigMine-12 - Held in Conjunction with SIGKDD Conference

作者： Parker, Charles BigML Inc. 2851 NW 9th St. Corvallis OR 97330 United States

ISBN: (纸本)9781450315470

In machine learning, scale adds complexity. The most obvious consequence of scale is that data takes longer to process. At certain points, however, scale makes trivial operations costly, thus forcing us to re-evaluate algorithms in light of the complexity of those operations. Here, we will discuss one important way a general large scale machine learning setting may differ from the standard supervised classification setting and show some the results of some preliminary experiments highlighting this difference. The results suggest that there is potential for significant improvement beyond obvious solutions. Copyright © 2012 ACM.

关键词： Supervised learning

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An empirical comparison of label prediction algorithms on automatically inferred networks

An empirical comparison of label prediction algorithms on au...

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1st international Conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Ali, Omar Zappella, Giovanni De Bie, Tijl Cristianini, Nello Intelligent Systems Laboratory Bristol University Bristol United Kingdom Dipartimento di Matematica'F. Enriques' Universitá Degli Studi di Milano Milan Italy

ISBN: (纸本)9789898425980

The task of predicting the label of a network node, based on the labels of the remaining nodes, is an area of growing interest in machine learning, as various types of data are naturally represented as nodes in a graph. As an increasing number of methods and approaches are proposed to solve this task, the problem of comparing their performance becomes of key importance. In this paper we present an extensive experimental comparison of 15 different methods, on 15 different labelled-networks, as well as releasing all datasets and source code. In addition, we release a further set of networks that were not used in this study (as not all benchmarked methods could manage very large datasets). Besides the release of data, protocols and algorithms, the key contribution of this study is that in each of the 225 combinations we tested, the best performance-both in accuracy and running time-was achieved by the same algorithm: Online Majority Vote. This is also one of the simplest methods to implement.

关键词： data mining

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Prototype selectioninimbalanced data for dissimilarity representation: A preliminary study

Prototype selectioninimbalanced data for dissimilarity repre...

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1st international Conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Millán-Giraldo, Mónica García, Vicente Sánchez, J. Salvador Institute of New Imaging Technologies Universitat Jaume I Av. Sos Baynat s/n 12071 Castellón de la Plana Spain

ISBN: (纸本)9789898425980

In classification problems, the dissimilarity representation has shown to be more robust than using the feature space. In order to build the dissimilarity space, a representation set of r objects is used. Several methods have been proposed for the selection of a suitable representation set that maximizes the classification performance. A recurring and crucial challenge in pattern recognition and machine learning refers to the class imbalance problem, which has been said to hinder the performance of learning algorithms. In this paper, we carry out a preliminary study that pursues to investigate the effects of several prototype selection schemes when data set are imbalanced, and also to foresee the benefits of selecting the representation set when the class imbalance is handled by resampling the data set. statistical analysis of experimental results using Friedman test demonstrates that the application of resampling significantly improve the performance classification.

关键词： learning algorithms

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