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283
Deep classifier for large scale hierarchical text classification
, 2010
"... Abstract. In this competition, we refined a novel algorithm for clas-sification on large scale documents with deep category structure based on a two-stage strategy known as the deep-classifier [1]. The basic idea of deep-classifier is to take advantage of the better performance of k-NN relevance sea ..."
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Cited by 1 (0 self)
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Abstract. In this competition, we refined a novel algorithm for clas-sification on large scale documents with deep category structure based on a two-stage strategy known as the deep-classifier [1]. The basic idea of deep-classifier is to take advantage of the better performance of k-NN relevance
Clustered Hierarchical Search Structure for Large-Scale Packet Classification on FPGA
"... Abstract—Most current SRAM-based high-speed Internet Protocol (IP) packet classification implementations use tree traversal and pipelining. However, these approaches result in inefficient memory utilization. Due to the limited amount of on-chip memory of the state-of-the-art Field Programmable Gate ..."
Abstract
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Cited by 1 (0 self)
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Arrays (FPGAs), existing designs cannot support large filter databases arising in backbone routers and intrusion detection systems. Hierarchical search structures for packet classification exhibit good memory performance and support quick rule update. However, pipelined hardware implementation
Improved hierarchical svms for large-scale hierarchical text classification challenge
- Website
"... Abstract. This paper describes an improved decision tree of multiclass SVM classifiers, with which we participate in the basic task and the cheap task of large-scale hierarchical text classification challenge. The structure of the decision tree is a simplified version of the hierarchy of the classes ..."
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Cited by 3 (0 self)
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Abstract. This paper describes an improved decision tree of multiclass SVM classifiers, with which we participate in the basic task and the cheap task of large-scale hierarchical text classification challenge. The structure of the decision tree is a simplified version of the hierarchy
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
"... Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object’s location one can take a sliding window approach, but this strongly increases the computational ..."
Abstract
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Cited by 120 (9 self)
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the computational cost, because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages
ClassView: Hierarchical Video Shot Classification, Indexing, and Accessing
- IEEE TRANS. ON MULTIMEDIA
, 2004
"... Recent advances in digital video compression and networks have made video more accessible than ever. However, the existing content-based video retrieval systems still suffer from the following problems. 1 ) Semantics---sensitive video classification problem because of the semantic gap between low-le ..."
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Cited by 42 (4 self)
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database indexing and summary presentation technique is proposed to support more effective video access over a large-scale database. The hierarchical tree structure of our video database indexing scheme is determined by the domain-dependent concept hierarchy which is also used for video classification
The Robustness of Content-Based Search in Hierarchical Peer to Peer Networks
- In Proceedings of the 13 th International Conference on Information and Knowledge Management (CIKM'04). RevConnect
, 2004
"... Hierarchical peer to peer networks with multiple directory services are an important architecture for large-scale file sharing due to their effectiveness and efficiency. Recent research argues that they are also an effective method of providing large-scale content-based federated search of text-base ..."
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Cited by 7 (1 self)
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Hierarchical peer to peer networks with multiple directory services are an important architecture for large-scale file sharing due to their effectiveness and efficiency. Recent research argues that they are also an effective method of providing large-scale content-based federated search of text-based
Indexing Hierarchical Structures Using Graph Spectra
, 2005
"... Hierarchical image structures are abundant in computer vision and have been used to encode part structure, scale spaces, and a variety of multiresolution features. In this paper, we describe a framework for indexing such representations that embeds the topological structure of a directed acyclic g ..."
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Cited by 55 (8 self)
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graph (DAG) into a low-dimensional vector space. Based on a novel spectral characterization of a DAG, this topological signature allows us to efficiently retrieve a promising set of candidates from a database of models using a simple nearest-neighbor search. We establish the insensitivity
Deep Classifier: Automatically Categorizing Search Results into Large-Scale Hierarchies ABSTRACT
"... Organizing Web search results into hierarchical categories facilitates users ’ browsing through Web search results, especially for ambiguous queries where the potential results are mixed together. Previous methods on search result classification are usually based on pre-training a classification mod ..."
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Cited by 9 (2 self)
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Organizing Web search results into hierarchical categories facilitates users ’ browsing through Web search results, especially for ambiguous queries where the potential results are mixed together. Previous methods on search result classification are usually based on pre-training a classification
A Novel Hybrid Candidate Group Search Genetic Clustering for Large Scale Data
"... Clustering is an unsupervised approach to extract hidden patterns from the datasets. There are certain challenges in clustering, though it is very much difficult to produce good clustering, researchers have provided the solutions through various hybrid approaches. The proposed work is based on enhan ..."
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on enhancing the clustering results by using two algorithms: First Candidate Group Search (CGS) is used to produce clusters and Genetic algorithm (GA). A CGS can be applied to large dataset with less computational time, but the drawback is it can’t results in global optima. Hence GA is used for further
Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods
"... We propose a highly efficient framework for penalized likelihood kernel methods applied to multi-class models with a large, structured set of classes. As opposed to many previous approaches which try to decompose the fitting problem into many smaller ones, we focus on a Newton optimization of the co ..."
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Cited by 8 (0 self)
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to new kernels, and focusing code optimization efforts to these primitives only. Kernel parameters are learned automatically, by maximizing the cross-validation log likelihood in a gradient-based way, and predictive probabilities are estimated. We demonstrate our approach on large scale text
Results 1 - 10
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283
