Support vector machines for spam categorization
Harris Drucker low, Donghui Wu, Vladimir Vapnik
The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
Citation edge evidence
- Machine learning in automated text categorization→Support vector machines for spam categorizationLegacy edge — source detail unavailable
- Semi-Supervised Learning→Support vector machines for spam categorizationLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→Support vector machines for spam categorizationLegacy edge — source detail unavailable
- Natural language processing: state of the art, current trends and challenges→Support vector machines for spam categorizationLegacy edge — source detail unavailable
- The text mining handbook: advanced approaches in analyzing unstructured data→Support vector machines for spam categorizationLegacy edge — source detail unavailable
- Adaptive Computation and Machine Learning→Support vector machines for spam categorizationLegacy edge — source detail unavailable
- Support vector machines for spam categorization→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Support vector machines for spam categorization→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
- Support vector machines for spam categorization→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
- Support vector machines for spam categorization→Experiments with a new boosting algorithmLegacy edge — source detail unavailable
- Support vector machines for spam categorization→A Comparative Study on Feature Selection in Text CategorizationLegacy edge — source detail unavailable
- Support vector machines for spam categorization→Fast Effective Rule InductionLegacy edge — source detail unavailable
- Semi-Supervised Learning→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
- Semi-Supervised Learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Fast Effective Rule Induction→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
- The text mining handbook: advanced approaches in analyzing unstructured data→Machine learning in automated text categorizationLegacy edge — source detail unavailable
- The text mining handbook: advanced approaches in analyzing unstructured data→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
- The text mining handbook: advanced approaches in analyzing unstructured data→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- The text mining handbook: advanced approaches in analyzing unstructured data→A Comparative Study on Feature Selection in Text CategorizationLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
- An introduction to kernel-based learning algorithms→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Experiments with a new boosting algorithm→Fast Effective Rule InductionLegacy edge — source detail unavailable
- Experiments with a new boosting algorithm→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
- Machine learning in automated text categorization→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
- Machine learning in automated text categorization→A Comparative Study on Feature Selection in Text CategorizationLegacy edge — source detail unavailable
- Text categorization with Support Vector Machines: Learning with many relevant features→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
- Text categorization with Support Vector Machines: Learning with many relevant features→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Text categorization with Support Vector Machines: Learning with many relevant features→A Comparative Study on Feature Selection in Text CategorizationLegacy edge — source detail unavailable
- Adaptive Computation and Machine Learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Adaptive Computation and Machine Learning→A Comparative Study on Feature Selection in Text CategorizationLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
Links
Topics
| Text and Document Classification Technologies | Computer Science |
| Spam and Phishing Detection | Computer Science |
| Face and Expression Recognition | Computer Science |
Is this record sound?
complete
Nothing in this record contradicts itself and no field we check is missing.
- supports3 author record(s) attached.
- supports27 reference(s) recorded.
- neutralThe DOI carries no year to check against.
- supportsA title is present.
Provenance
sha256 88a60cdbb94c6a13…