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SUNRISE: Improving the Performance of the RISE Algorithm
"... Abstract. Ideally, a multistrategy learning algorithm performs better than its component approaches. RISE is a multistrategy algorithm that combines rule induction and instancebased learning. It achieves higher accuracy than some stateoftheart learning algorithms, but for large data sets it ha ..."
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Abstract. Ideally, a multistrategy learning algorithm performs better than its component approaches. RISE is a multistrategy algorithm that combines rule induction and instancebased learning. It achieves higher accuracy than some stateoftheart learning algorithms, but for large data sets
A densitybased algorithm for discovering clusters in large spatial databases with noise
, 1996
"... Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clu ..."
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Cited by 1741 (70 self)
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Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery
Observations on the dynamics of a congestion control algorithm: The effects of twoway traffic
 In Proceedings of ACM SIGCOMM
, 1991
"... We use simulation to study the dynamics of the congestion cent rol algorithm embedded in the BSD 4.3Tahoe TCP implementation. We investigate the simple case of a few TCP connections, originating and terminating at the same pair of hosts, using a single bottleneck link. This work is an extension of ..."
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Cited by 382 (6 self)
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We use simulation to study the dynamics of the congestion cent rol algorithm embedded in the BSD 4.3Tahoe TCP implementation. We investigate the simple case of a few TCP connections, originating and terminating at the same pair of hosts, using a single bottleneck link. This work is an extension
Xmeans: Extending Kmeans with Efficient Estimation of the Number of Clusters
 In Proceedings of the 17th International Conf. on Machine Learning
, 2000
"... Despite its popularity for general clustering, Kmeans suffers three major shortcomings; it scales poorly computationally, the number of clusters K has to be supplied by the user, and the search is prone to local minima. We propose solutions for the first two problems, and a partial remedy for the t ..."
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Cited by 412 (5 self)
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) measure. The innovations include two new ways of exploiting cached sufficient statistics and a new very efficient test that in one Kmeans sweep selects the most promising subset of classes for refinement. This gives rise to a fast, statistically founded algorithm that outputs both the number of classes
Greedy layerwise training of deep networks
, 2006
"... Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multilayer neural networks have many levels of nonlinearities allow ..."
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Cited by 385 (47 self)
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introduced a greedy layerwise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this algorithm empirically and explore variants to better understand its success
Measuring praise and criticism: Inference of semantic orientation from association
 ACM Transactions on Information Systems
, 2003
"... The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., “honest”, “intrepid”) and negative semantic orientation indicates criticism (e.g., “disturbing”, “superfluous”). Semantic orientation varies in both direction (positive or neg ..."
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Cited by 303 (6 self)
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(LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8 % on the full test set, but the accuracy rises above 95 % when
Gossip algorithms: Design, analysis and applications
, 2005
"... Motivated by applications to sensor, peertopeer and ad hoc networks, we study distributed asynchronous algorithms, also known as gossip algorithms, for computation and information exchange in an arbitrarily connected network of nodes. Nodes in such networks operate under limited computational, co ..."
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Cited by 220 (13 self)
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, communication and energy resources. These constraints naturally give rise to "gossip" algorithms: schemes which distribute the computational burden and in which a node communicates with a randomly chosen neighbor. We analyze the averaging problem under the gossip constraint for arbitrary network
Review: Protein Secondary Structure Prediction Continues to Rise
 J. Struct. Biol
, 2001
"... f prediction accuracy? We shall see. 2001 Academic Press INTRODUCTION History. Linus Pauling correctly guessed the formation of helices and strands (14, 15) (and falsely hypothesized other structures). Three years before Pauling's guess was verified by the publications of the first Xray stru ..."
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Cited by 179 (22 self)
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propensities for segments of 351 adjacent residues (19). Basically any imaginable theoretical algorithm had been applied to the problem of predicting secondary structure from sequence. However, it seemed that prediction accuracy stalled at levels slightly above 60% (percentage of residues predicted correctly
A Comparison of Dynamic Branch Predictors that use Two Levels of Branch History
 in Proceedings of the 20th Annual International Symposium on Computer Architecture
, 1993
"... Recent attention to speculative execution as a mechanism for increasing performance of single instruction streams has demanded substantially better branch prediction than what has been previously available. We [1, 2] and Pan, So, and Rahmeh [4] have both proposed variations of the same aggressive dy ..."
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Cited by 279 (9 self)
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Recent attention to speculative execution as a mechanism for increasing performance of single instruction streams has demanded substantially better branch prediction than what has been previously available. We [1, 2] and Pan, So, and Rahmeh [4] have both proposed variations of the same aggressive dynamic branch predictor for handling those needs. We call the basic model TwoLevel Adaptive Branch Prediction; Pan, So, and Rahmeh call it Correlation Branch Prediction. In this paper, we adopt the terminology of [2] and show that there are really nine variations of the same basic model. We compare the nine variations with respect to the amount of history information kept. We study the effects of different branch history lengths and pattern history table configurations. Finally, we evaluate the cost effectiveness of the nine variations. 1 Introduction With the current movement toward deeper pipelines and wider issue rates, extremely high branch prediction accuracy becomes critical because a...
Deriving Intrinsic Images from Image Sequences
, 2001
"... Intrinsic images are a useful midlevel description of scenes proposed by Barrow and Tenebaum [1]. An image is decomposed into two images: a reflectance image and an illumination image. Finding such a decomposition remains a difficult problem in computer vision. Here we focus on a slightly easier pro ..."
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Cited by 251 (5 self)
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work on the statistics of natural images, we use a prior that assumes that illumination images will give rise to sparse filter outputs. We show that this leads to a simple, novel algorithm for recovering reflectance images. We illustrate the algorithm's performance on real and synthetic image
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