Results 1  10
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1,598
Indexing for data models with constraints and classes
 Journal of Computer and System Sciences
, 1996
"... We examine I Oefficient data structures that provide indexing support for new data models. The database languages of these models include concepts from constraint programming (e.g., relational tuples are generated to conjunctions of constraints) and from objectoriented programming (e.g., objects a ..."
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Cited by 110 (18 self)
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are organized in class hierarchies). Let n be the size of the database, c the number of classes, B the page size on secondary storage, and t the size of the output of a query: (1) Indexing by one attribute in many constraint data models is equivalent to external dynamic interval management, which is a special
Indexing for Data Models with Constraints and Classes
, 1993
"... We examine I/Oefficient data structures that provide indexing support for new data models. ..."
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We examine I/Oefficient data structures that provide indexing support for new data models.
The nesC language: A holistic approach to networked embedded systems
 In Proceedings of Programming Language Design and Implementation (PLDI
, 2003
"... We present nesC, a programming language for networked embedded systems that represent a new design space for application developers. An example of a networked embedded system is a sensor network, which consists of (potentially) thousands of tiny, lowpower “motes, ” each of which execute concurrent, ..."
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Cited by 943 (48 self)
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, reactive programs that must operate with severe memory and power constraints. nesC’s contribution is to support the special needs of this domain by exposing a programming model that incorporates eventdriven execution, a flexible concurrency model, and componentoriented application design. Restrictions
Using Maximum Entropy for Text Classification
, 1999
"... This paper proposes the use of maximum entropy techniques for text classification. Maximum entropy is a probability distribution estimation technique widely used for a variety of natural language tasks, such as language modeling, partofspeech tagging, and text segmentation. The underlying principl ..."
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Cited by 326 (6 self)
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principle of maximum entropy is that without external knowledge, one should prefer distributions that are uniform. Constraints on the distribution, derived from labeled training data, inform the technique where to be minimally nonuniform. The maximum entropy formulation has a unique solution which can
Ultraconservative Online Algorithms for Multiclass Problems
 Journal of Machine Learning Research
, 2001
"... In this paper we study online classification algorithms for multiclass problems in the mistake bound model. The hypotheses we use maintain one prototype vector per class. Given an input instance, a multiclass hypothesis computes a similarityscore between each prototype and the input instance and th ..."
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Cited by 320 (21 self)
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In this paper we study online classification algorithms for multiclass problems in the mistake bound model. The hypotheses we use maintain one prototype vector per class. Given an input instance, a multiclass hypothesis computes a similarityscore between each prototype and the input instance
Image classification for contentbased indexing
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2001
"... Grouping images into (semantically) meaningful categories using lowlevel visual features is a challenging and important problem in contentbased image retrieval. Using binary Bayesian classifiers, we attempt to capture highlevel concepts from lowlevel image features under the constraint that the ..."
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Cited by 227 (2 self)
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into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the classconditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated
Systems with finite communication bandwidth constraints  II: Stabilization with limited information feedback
, 1999
"... In this paper a new class of feedback control problems is introduced. Unlike classical models, the systems considered here have communication channel constraints. As a result, the issue of coding and communication protocol becomes an integral part of the analysis. Since these systems cannot be asymp ..."
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Cited by 224 (5 self)
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In this paper a new class of feedback control problems is introduced. Unlike classical models, the systems considered here have communication channel constraints. As a result, the issue of coding and communication protocol becomes an integral part of the analysis. Since these systems cannot
Scheduling Dynamic Dataflow Graphs With Bounded Memory Using The Token Flow Model
, 1993
"... This paper builds upon research by Lee [1] concerning the token flow model, an analytical model for the behavior of dataflow graphs with datadependent control flow, by analyzing the properties of cycles of the schedule: sequences of actor executions that return the graph to its initial state. Neces ..."
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Cited by 232 (5 self)
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This paper builds upon research by Lee [1] concerning the token flow model, an analytical model for the behavior of dataflow graphs with datadependent control flow, by analyzing the properties of cycles of the schedule: sequences of actor executions that return the graph to its initial state
Support vector machines: Training and applications
 A.I. MEMO 1602, MIT A. I. LAB
, 1997
"... The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Laboratories [3, 6, 8, 24]. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and MultiLayer Perc ..."
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Cited by 223 (3 self)
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of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Since Structural Risk Minimization is an inductive principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing the Mean Square Error over the data set
Distributed regression: an efficient framework for modeling sensor network data
 In IPSN
, 2004
"... We present distributed regression, an efficient and general framework for innetwork modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon kernel linear regression, w ..."
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Cited by 178 (8 self)
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, where the model takes the form of a weighted sum of local basis functions; this provides an expressive yet tractable class of models for sensor network data. Rather than transmitting data to one another or outside the network, nodes communicate constraints on the model parameters, drastically reducing
Results 1  10
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1,598