Nowadays, the usage of neural network strategies in patternrecognition is a widely considered solution. In this paper we propose three different strategies to select more efficiently the patterns for a fast learning ...
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data clustering aims at finding the hidden patterns in a large collection of data and a large body of effective algorithms have been proposed to partition the data in the past three decades. However, most of the algor...
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ISBN:
(纸本)9780769539232
data clustering aims at finding the hidden patterns in a large collection of data and a large body of effective algorithms have been proposed to partition the data in the past three decades. However, most of the algorithms fail to handle data that expose a manifold structure which is common in many data-driven application, such as interpretation and recognition of video, handwritten character and image data. In this paper, we study the problem of clustering on manifold that aims to partition a set of input data into several clusters each of which contains data points from a simple low-dimensional manifold. We apply the basic assumption of local and global consistency on the manifold. A novel algorithm name CMLGC is proposed to find the proper clusters on the manifold. Our research can also be seen as an instance of manifold learning. The encouraging results on several synthetic and real-world data set are obtained which validate our proposed algorithm.
The proceedings contain 5 papers. The topics discussed include: do not feel the trolls;end-user programming and the advent of sharable, social machines;computing FOAF co-reference relations with rules and machine lear...
The proceedings contain 5 papers. The topics discussed include: do not feel the trolls;end-user programming and the advent of sharable, social machines;computing FOAF co-reference relations with rules and machinelearning;mapping tweets to conference talks: a goldmine for semantics;and extracting semantic relations for mining of social data.
We describe a machinelearning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks on adver...
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ISBN:
(纸本)9781605588896
We describe a machinelearning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks on advertisements. We present a novel approach using translation models to learn user click propensity from sparse click logs. Our relevance predictions are then applied to multiple sponsored search applications in both offline editorial evaluations and live online user tests. The predicted relevance score is used to improve the quality of the search page in three areas: filtering low quality ads, more accurate ranking for ads, and optimized page placement of ads to reduce prominent placement of low relevance ads. We show significant gains across all three tasks. Copyright 2010 ACM.
In order to develop an automatic and rapid detection method for enumeration of total bacteria in juice, biomimetic patternrecognition and machine vision were employed. The characteristic data, such as shape, texture ...
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Sensors are being deployed to improve border security generating enormous collections of data and databases. Unfortunately these sensors can respond to a variety of stimuli, sometimes reacting to meaningful events and...
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Risk Management is a logical and systematic method of identifying, analyzing, treating and monitoring the risks involved in any activity or process. The key to successful risk management lies in the ability to tailor ...
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The fuzzy neural network technology is one of the hot topics of datamining. According to the Max Similarity Rule, this paper sets forth the cross entropy theory with formulae deduction in detail and a new activation ...
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The overwhelming amount of data that is available nowadays makes many of the existing machine laming algorithms inapplicable to many real-world problems Two approaches have been used to deal with this problem scaling ...
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ISBN:
(纸本)9783642130243
The overwhelming amount of data that is available nowadays makes many of the existing machine laming algorithms inapplicable to many real-world problems Two approaches have been used to deal with this problem scaling up datamining algorithms [1] and data reduction Nevertheless. scaling up a certain algorithm is not always feasible. One of the most common methods for data reduction is feature selection. but when we face large problems, the scalability becomes an issue This paper presents a way of removing this difficulty using several rounds of feature selection on subsets of the original dataset, combined using a voting scheme The performance is very good in terms of testing error and storage reduction, while the execution time of the process is decreased very significantly The method is especially efficient when we use feature selection algorithms that are of a high computational cost An extensive comparison in 27 datasets of medium and large sizes front the UCI machinelearning Repository and using different classifiers shows the usefulness of our method.
The proceedings contain 86 papers. The topics discussed include: iris features extraction using dual-tree complex wavelet transform;fuzzy methods for forensic data analysis;a new weighted rough set framework for imbal...
ISBN:
(纸本)9781424478958
The proceedings contain 86 papers. The topics discussed include: iris features extraction using dual-tree complex wavelet transform;fuzzy methods for forensic data analysis;a new weighted rough set framework for imbalance class distribution;multi stereo camera data fusion for fingertip detection in gesture recognition systems;recognition of signed expressions using visually-oriented subunits obtained by an immune-based optimization;3-D object recognition based on SVM and stereo-vision: application in endoscopic imaging;inter-camera color calibration for object re-identification and tracking;improving the accuracy of intrusion detection systems by using the combination of machinelearning approaches;mining web videos for video quality assessment;ultra fast fingerprint indexing for embedded system;damageless image hashing using neural network;and classification by means of fuzzy analogy-related proportions - a preliminary report.
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