Results 1 - 10 of 1,333

Multi-Prototype Vector-Space Models of Word Meaning

by Joseph Reisinger, Raymond J. Mooney
"... Current vector-space models of lexical semantics create a single “prototype ” vector to represent the meaning of a word. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. This paper presents a method that uses clustering to produce multiple “sense-specific ..."
Abstract - Cited by 47 (2 self) - Add to MetaCart
Current vector-space models of lexical semantics create a single “prototype ” vector to represent the meaning of a word. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. This paper presents a method that uses clustering to produce multiple “sense

Dynamic and Static Prototype Vectors for Semantic Composition

by Siva Reddy , Ioannis P Klapaftis , Diana Mccarthy , Suresh Manandhar - In Proceedings of the 5th International Joint Conference on Natural Language Processing , 2011
"... Abstract Compositional Distributional Semantic methods model the distributional behavior of a compound word by exploiting the distributional behavior of its constituent words. In this setting, a constituent word is typically represented by a feature vector conflating all the senses of that word. Ho ..."
Abstract - Cited by 19 (1 self) - Add to MetaCart
. However, not all the senses of a constituent word are relevant when composing the semantics of the compound. In this paper, we present two different methods for selecting the relevant senses of constituent words. The first one is based on Word Sense Induction and creates a static multi prototype vectors

Classification by Set Cover: The Prototype Vector Machine

by Jacob Bien, Robert Tibshirani , 2009
"... We introduce a new nearest-prototype classifier, the prototype vector machine (PVM). It arises from a combinatorial optimization problem which we cast as a variant of the set cover problem. We propose two algorithms for approximating its solution. The PVM selects a relatively small number of represe ..."
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We introduce a new nearest-prototype classifier, the prototype vector machine (PVM). It arises from a combinatorial optimization problem which we cast as a variant of the set cover problem. We propose two algorithms for approximating its solution. The PVM selects a relatively small number

Prototype Vector Machine for Large Scale Semi-Supervised Learning

by Kai Zhang, James T. Kwok, Bahram Parvin
"... Practical data mining rarely falls exactly into the supervised learning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computational intensiveness of graph-based SSL arises largely from the manifold or grap ..."
Abstract - Cited by 20 (4 self) - Add to MetaCart
or graph regularization, which in turn lead to large models that are difficult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highly scalable, graph-based algorithm for large-scale SSL. Our key innovation is the use of “prototypes vectors ” for efficient approximation

M-tree: An Efficient Access Method for Similarity Search in Metric Spaces

by Paolo Ciaccia, Marco Patella, Pavel Zezula , 1997
"... A new access meth d, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
Abstract - Cited by 663 (38 self) - Add to MetaCart
of objects and split management, whF h keep th M-tree always balanced - severalheralvFV split alternatives are considered and experimentally evaluated. Algorithd for similarity (range and k-nearest neigh bors) queries are also described. Results from extensive experimentationwith a prototype system

Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines

by Kai Zhang, Liang Lan, James T. Kwok, Slobodan Vucetic, Bahram Parvin , 2013
"... Abstract — When the amount of labeled data are limited, semi-supervised learning can improve the learner’s performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximati ..."
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of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel

Ultraconservative Online Algorithms for Multiclass Problems

by Koby Crammer, Yoram Singer - 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 similarity-score between each prototype and the input instance and th ..."
Abstract - Cited by 320 (21 self) - Add to MetaCart
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 similarity-score between each prototype and the input instance

Svm-knn: Discriminative nearest neighbor classification for visual category recognition

by Hao Zhang, Alexander C. Berg, Michael Maire, Jitendra Malik - in CVPR , 2006
"... We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While n ..."
Abstract - Cited by 342 (10 self) - Add to MetaCart
We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While

Beacon Vector Routing: Scalable Point-to-Point in Wireless Sensornets

by Rodrigo Fonseca, Rodrigo Fonseca, Sylvia Ratnasamy, Sylvia Ratnasamy, David Culler, David Culler, Scott Shenker, Scott Shenker, Ion Stoica, Ion Stoica , 2004
"... This paper proposes a practical and scalable technique for point-to-point routing in wireless sensornets. This method, called Beacon Vector Routing (BVR), assigns coordinates to nodes based on the vector of distances (hop count) to a small set of beacons, and then defines a distance metric on these ..."
Abstract - Cited by 182 (14 self) - Add to MetaCart
on these coordinates. Packets are routed greedily, being forwarded to the next hop that is the closest (according to this beacon vector distance metric) to the destination. This approach is evaluated through both simulation and a prototype implementation on motes.

Multi-prototype support vector machine

by Fabio Aiolli - In Proceedings of International Joint Conference of Artificial Intelligence (IJCAI , 2003
"... We extend multiclass SVM to multiple prototypes per class. For this framework, we give a compact constrained quadratic problem and we suggest an efficient algorithm for its optimization that guarantees a local minimum of the objective function. An annealed process is also proposed that helps to esca ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
We extend multiclass SVM to multiple prototypes per class. For this framework, we give a compact constrained quadratic problem and we suggest an efficient algorithm for its optimization that guarantees a local minimum of the objective function. An annealed process is also proposed that helps
Results 1 - 10 of 1,333