Results 1  10
of
3,278
The Ant System: Optimization by a colony of cooperating agents
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSPART B
, 1996
"... An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant System. We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation ..."
Abstract

Cited by 1300 (46 self)
 Add to MetaCart
computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed
Maximizing the Spread of Influence Through a Social Network
 In KDD
, 2003
"... Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in gametheoretic settings, and the effects of ..."
Abstract

Cited by 990 (7 self)
 Add to MetaCart
, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target? We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NPhard here, and we provide
For Most Large Underdetermined Systems of Linear Equations the Minimal ℓ1norm Solution is also the Sparsest Solution
 Comm. Pure Appl. Math
, 2004
"... We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so that ..."
Abstract

Cited by 568 (10 self)
 Add to MetaCart
. In contrast, heuristic attempts to sparsely solve such systems – greedy algorithms and thresholding – perform poorly in this challenging setting. The techniques include the use of random proportional embeddings and almostspherical sections in Banach space theory, and deviation bounds for the eigenvalues
Neural NetworkBased Face Detection
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1998
"... We present a neural networkbased upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present ..."
Abstract

Cited by 1206 (22 self)
 Add to MetaCart
We present a neural networkbased upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present
Similarity search in high dimensions via hashing
, 1999
"... The nearest or nearneighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over highdimensional data, e.g., image dat ..."
Abstract

Cited by 641 (10 self)
 Add to MetaCart
The nearest or nearneighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over highdimensional data, e.g., image
ModelDriven Data Acquisition in Sensor Networks
 IN VLDB
, 2004
"... Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor ..."
Abstract

Cited by 449 (36 self)
 Add to MetaCart
of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomialtime heuristic for identifying solutions that perform well in practice. We evaluate our approach
Policy gradient methods for reinforcement learning with function approximation.
 In NIPS,
, 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
Abstract

Cited by 439 (20 self)
 Add to MetaCart
into estimating a value function, with the actionselection policy represented implicitly as the "greedy" policy with respect to the estimated values (e.g., as the policy that selects in each state the action with highest estimated value). The valuefunction approach has worked well in many applications
Greedy Attribute Selection
 In Proceedings of the Eleventh International Conference on Machine Learning
, 1994
"... Many realworld domains bless us with a wealth of attributes to use for learning. This blessing is often a curse: most inductive methods generalize worse given too many attributes than if given a good subset of those attributes. We examine this problem for two learning tasks taken from a calendar s ..."
Abstract

Cited by 218 (2 self)
 Add to MetaCart
scheduling domain. We show that ID3/C4.5 generalizes poorly on these tasks if allowed to use all available attributes. We examine five greedy hillclimbing procedures that search for attribute sets that generalize well with ID3/C4.5. Experiments suggest hillclimbing in attribute space can yield substantial
Greedy Heuristics for Degenerate Primer Selection ⋆
"... Abstract. Amplification of multiple DNA sequences in a single Polymerase Chain Reaction (PCR) experiment, called Multiplex PCR (MPPCR), is a widely known laboratory technique that requires efficient selection of a set of synthetic strings called Degenerate Primers to result in successful PCR produc ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
that have been shown to perform well in practice on real biological data. In this paper, we present two new greedy heuristics for the problem, analyze their time and space complexities and compare the performance of one of the proposed algorithms on random and real biological data with that of two
Feature Subset Selection Using A Genetic Algorithm
, 1997
"... : Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This is due to the fact that the performance of the classifier (usually induced by some learning algorithm) ..."
Abstract

Cited by 279 (7 self)
 Add to MetaCart
: Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This is due to the fact that the performance of the classifier (usually induced by some learning algorithm
Results 1  10
of
3,278