Abstract
This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take advantage of the hierarchy by considering only second-level categories that exceed a threshold at the top level.
Keywords
HierarchyComputer scienceHierarchical database modelHierarchical control systemInformation retrievalHierarchical organizationContent (measure theory)Multilevel modelArtificial intelligenceData miningMachine learningMathematicsControl (management)
Affiliated Institutions
Related Publications
SUN attribute database: Discovering, annotating, and recognizing scene attributes
In this paper we present the first large-scale scene attribute database. First, we perform crowd-sourced human studies to find a taxonomy of 102 discriminative attributes. Next,...
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semi-supervised manner: the model is learnt from example images ...
The Generalized A* Architecture
We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in ...
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two as...
Nested Cladistic Analysis Indicates Population Fragmentation Shapes Genetic Diversity in a Freshwater Mussel
Abstract Recently developed phylogeographic analyses that incorporate genealogical relationships of alleles offer the exciting prospect of disentangling historical from contempo...
Publication Info
- Year
- 2000
- Type
- article
- Pages
- 256-263
- Citations
- 801
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
801
OpenAlex
Cite This
Susan Dumais,
Hao Chen
(2000).
Hierarchical classification of Web content.
, 256-263.
https://doi.org/10.1145/345508.345593
Identifiers
- DOI
- 10.1145/345508.345593