44 Result(s)
within
Conference Paper
**gbo Zhou
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Chapter and Conference Paper
The Classification of Traditional Chinese Painting Based on CNN
Traditional Chinese painting has an extremely long and uninterrupted history, which makes it valuable for people to study and analyze it. Meanwhile, the convolutional neural network has achieved a great succes...
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Chapter and Conference Paper
ExtTra: Short-Term Traffic Flow Prediction Based on Extremely Randomized Trees
Short-term traffic flow prediction is an important task for intelligent transportation systems. Conventional time series based approaches such as ARIMA can hardly reflect the inter-dependence of related roads....
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Chapter and Conference Paper
The Research and Implementation of the Fine-Grained Implicit Authentication Framework for Android
Nowadays, in order to protect sensitive information in Android apps, plenty of identity authentication techniques were developed, including the password, graphical-password, fingerprinting, etc. Unfortunately...
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Chapter and Conference Paper
An Image Enhancement Algorithm Based on Fractional-Order Relaxation Oscillator
In this paper, a cortex rhythms mimicking in fractional-order Relaxation oscillator is implemented and the existence of the rhythm is proved. Furthermore, the Quasi Gamma Curve (QGC) model is established based...
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Chapter and Conference Paper
App Uninstalls Prediction: A Machine Learning and Time Series Mining Approach
Nowadays mobile applications (a.k.a. app) are playing unprecedented important roles in our daily life and their research has attracted many scholars. However, traditional research mainly focuses on mining app ...
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Chapter and Conference Paper
Effective Influence Maximization Based on the Combination of Multiple Selectors
Influence maximization is an extensively studied optimization problem aiming at finding the best k seed nodes in a network such that they can influence the maximum number of individuals. Traditional heuristic ...
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Chapter and Conference Paper
A Linear Time Algorithm for Influence Maximization in Large-Scale Social Networks
Influence maximization is the problem of finding k seed nodes in a given network as information sources so that the influence cascade can be maximized. To solve this problem both efficiently and effectively, i...
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Chapter and Conference Paper
sonSQL: An Extensible Relational DBMS for Social Network Start-Ups
There is now a proliferation of social network start-ups. This demonstration introduces sonSQL, a MySQL variant that aims to be the default off-the-shelf database system for managing their social network data.
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Chapter and Conference Paper
Challenges in Representation Learning: A Report on Three Machine Learning Contests
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge...
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Chapter and Conference Paper
Study on Application of Scale Invariant Feature Transform Algorithm on Automated Geometric Correction of Remote Sensing Images
In recent years, data collected from remote sensing satellite and aerophotography have been showing a geometric sequence increase. A method of Scale Invariant Feature Transform (SIFT) algorithm could be employ...
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Chapter and Conference Paper
sonSchema: A Conceptual Schema for Social Networks
sonSQL is a MySQL variant that aims to be the default database system for social network data. It uses a conceptual schema called sonSchema to translate a social network design into logical tables. This paper ...
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Chapter and Conference Paper
Finding and Extracting Academic Information from Conference Web Pages
This paper proposes a method for finding and extracting academic information from conference Web pages. The main contributions include: (1) A lightweight topic crawling method based on search engine is used to...
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Chapter and Conference Paper
A Linked Data Generation Method for Academic Conference Websites
This paper proposes an automatic method for extracting information from academic conference Web pages, and organizes these information as ontologies, then matches these ontologies to the academic linked data. ...
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Chapter and Conference Paper
Summarizing Semantic Associations Based on Focused Association Graph
As the explosive growth of online linked data, there is an urgent need for an efficient approach to discovering and understanding various semantic associations. Research has been done on discovering semantic a...
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Chapter and Conference Paper
Development and Application of a Farmland Test Data Processing System Designed for Wireless Sensor Network Applications
Data collected by measuring farmland environmental indicators are vital sources of agricultural information. Wireless sensor networks (WSNs) have been employed to acquire stable and real time farmland environm...
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Chapter and Conference Paper
Mining Link Patterns in Linked Data
As the explosive growth of online linked data, an emerging problem is what and how we can learn from these data. An important knowledge we can obtain is the link patterns among objects, which are helpful for c...
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Chapter and Conference Paper
Agricultural Landscape Dynamics and Its Response in Seasonal Vegetation Activities in the Loess Plateau, Northern Shaanxi, China
The ecological and environmental conditions in semiarid areas are closely linked to landscape dynamics. This study examined the seasonal vegetation activities of landscape classes and dynamics in the Loess Pla...
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Chapter and Conference Paper
Morlet-RBF SVM Model for Medical Images Classification
Map** way plays a significant role in Support Vector Machine (SVM). An appropriate map** can make data distribution in higher dimensional space easily separable. In this paper Morlet-RBF kernel model is pr...
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Chapter and Conference Paper
An Empirical Comparison of Two Boosting Algorithms on Real Data Sets Based on Analysis of Scientific Materials
Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. In this paper, multiple TAN classifiers generated by GTAN are combined by a combination me...
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Chapter and Conference Paper
An Empirical Comparison of Two Boosting Algorithms on Real Data Sets with Artificial Class Noise
Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. In this paper, multiple TAN classifiers generated by GTAN are combined by a combination me...
