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2,411,148
The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 12976 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Maximum likelihood from incomplete data via the EM algorithm
- JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11807 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
Integrity and Privacy Preserving Data Aggregation Algorithm for WSNs
"... Abstract. This paper proposed an integrity and privacy preserving data aggregation algorithm for WSNs, which is called IPPDA. First, it attached a group of congruent numbers to the sensing data in order to execute integrity checking operated by sink node using Chinese remainder theorem (CRT); then i ..."
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Abstract. This paper proposed an integrity and privacy preserving data aggregation algorithm for WSNs, which is called IPPDA. First, it attached a group of congruent numbers to the sensing data in order to execute integrity checking operated by sink node using Chinese remainder theorem (CRT
A Survey of Distributed Data Aggregation Algorithms,” University of Minho
, 2011
"... Distributed data aggregation is an important task, allowing the de-centralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting val-ues result from the distributed computation of functions like count, sum and average. S ..."
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Cited by 10 (1 self)
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to the considerable amount and variety of ag-gregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, jus-tifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data
Optimal Aggregation Algorithms for Middleware
- IN PODS
, 2001
"... Assume that each object in a database has m grades, or scores, one for each of m attributes. For example, an object can have a color grade, that tells how red it is, and a shape grade, that tells how round it is. For each attribute, there is a sorted list, which lists each object and its grade under ..."
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Cited by 701 (4 self)
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under that attribute, sorted by grade (highest grade first). There is some monotone aggregation function, or combining rule, such as min or average, that combines the individual grades to obtain an overall grade. To determine the top k objects (that have the best overall grades), the naive algorithm
Delay Efficient Distributed Data Aggregation Algorithm in Wireless Sensor Networks
"... In wireless sensor networks data aggregation is a very important one and at the same time the aggregation should be energy efficient and a lesser amount of delay. To solve the problem we propose Delay Efficient Distributed Data Aggregation (DEDA) Scheduling Algorithm for Wireless Sensor Network (WSN ..."
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In wireless sensor networks data aggregation is a very important one and at the same time the aggregation should be energy efficient and a lesser amount of delay. To solve the problem we propose Delay Efficient Distributed Data Aggregation (DEDA) Scheduling Algorithm for Wireless Sensor Network
Threshold Based Data Aggregation Algorithm To Detect Rainfall Induced Landslides
- In Proceedings of the 2008 International Conference on Wireless Networks (ICWN’08
, 2008
"... Landslides are one of the environmental disas-ters that cause massive destruction of human life and infrastructure. Real time monitoring of a land-slide prone areas are necessary to issue fore warn-ing. To accomplish real time monitoring, massive amount of data have to be collected and analyzed with ..."
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Cited by 6 (0 self)
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within a short span of time. This work has de-veloped a method for effective data collection and aggregation by implementing threshold alert lev-els. The sampling rates of threshold alert levels will determine amount of data collected and aggre-gated which will reduce the power consumption by each
A Data Aggregation Algorithm Based on Splay Tree for Wireless Sensor Networks
"... Abstract-Detecting the region of emergent events is an important application of wireless sensor networks (WSN). One of the key challenges in detecting event in a WSN is how to detect it accurately while transmitting minimum information to provide sufficient details about the event. In this paper, an ..."
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, an aggregation algorithm based on splay tree is proposed to achieve the following goals: monitoring data of any portion of the region can be obtained at one time by querying the root instead of flooding those regions, thus incurring significant energy savings. The performance and cost of the algorithm
An Efficient Data Aggregation Algorithm for Cluster-based Sensor Network
"... Abstract — Data aggregation in wireless sensor networks eliminates redundancy to improve bandwidth utilization and energyefficiency of sensor nodes. One node, called the cluster leader, collects data from surrounding nodes and then sends the summarized information to upstream nodes. In this paper, w ..."
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, we propose an algorithm to select a cluster leader that will perform data aggregation in a partially connected sensor network. The algorithm reduces the traffic flow inside the network by adaptively selecting the shortest route for packet routing to the cluster leader. We also describe a simulation
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
Results 1 - 10
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