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668
Knowledge-based Analysis of Microarray Gene Expression Data By Using Support Vector Machines
, 2000
"... We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of ..."
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Cited by 520 (8 self)
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of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression
Improving Network Intrusion Detection with Growing Hierarchical Self-Organizing Maps
"... Abstract- Nowadays, the growth of the computer networks and the expansion of the Internet have made the security to be a critical issue. In fact, many proposals for Intrusion Detection/Prevention Systems (IDS/IPS) have been proposed. These proposals try to avoid that corrupt or anomalous traffic rea ..."
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Cited by 2 (0 self)
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reaches the user application or the operating system. Nevertheless, most of the IDS/IPS proposals only distinguish between normal traffic and anomalous traffic that can be suspected to be a potential attack. In this paper, we present a IDS/IPS approach based on Growing Hierarchical Self-Organizing Maps
Action Recognition based on Hierarchical Self-Organizing Maps
"... Abstract. We propose a hierarchical neural architecture able to recog-nise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It ..."
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Abstract. We propose a hierarchical neural architecture able to recog-nise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space
Clustering of Self-Organizing Map
"... Abstract. In this paper, we present a new similarity measure for a clustering self-organizing map which will be reached using a new approach of hierarchical clustering. (1) The similarity measure is composed from two terms: weighted Ward distance and Euclidean distance weighted by neighbourhood func ..."
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Abstract. In this paper, we present a new similarity measure for a clustering self-organizing map which will be reached using a new approach of hierarchical clustering. (1) The similarity measure is composed from two terms: weighted Ward distance and Euclidean distance weighted by neighbourhood
Self-Organizing Hierarchical Routing for Scalable . . .
"... As devices with wireless networking become more pervasive, mobile ad hoc networks are becoming increasingly important, motivating the development of highly scalable ad hoc networking techniques. In this paper, we present the design and evaluation of a novel protocol for scalable routing in ad hoc ne ..."
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networks as part of the Safari project. We develop a probabilistic, self-organizing network hierarchy formation protocol that recursively forms the nodes of the ad hoc network into an adaptive, proximity-based hierarchy of cells. We develop a hybrid routing protocol that uses this hierarchy as well as on
The Hierarchical Isometric SelfOrganizing Map for Manifold Representation
- IEEE Conference on Computer Vision and Pattern Recognition, 17-22 June 2007, Page 1 – 8
"... We present an algorithm, Hierarchical ISOmetric Self-Organizing Map (H-ISOSOM), for a concise, organized manifold representation of complex, non-linear, large scale, high-dimensional input data in a low dimensional space. The main contribution of our algorithm is threefold. First, we modify the prev ..."
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Cited by 3 (0 self)
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We present an algorithm, Hierarchical ISOmetric Self-Organizing Map (H-ISOSOM), for a concise, organized manifold representation of complex, non-linear, large scale, high-dimensional input data in a low dimensional space. The main contribution of our algorithm is threefold. First, we modify
GROWING HIERARCHICAL SELF ORGANIZING MAP
"... Intrusion Detection System (IDS) protects a system by detecting “known ” as well as “unknown ” attacks and generates the alert for suspicious activities in the traffic. There are various approaches for IDS, but our survey was focused on IDS using Self Organizing Map (SOM). Our survey shows that the ..."
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Intrusion Detection System (IDS) protects a system by detecting “known ” as well as “unknown ” attacks and generates the alert for suspicious activities in the traffic. There are various approaches for IDS, but our survey was focused on IDS using Self Organizing Map (SOM). Our survey shows
Dynamic intrusion detection using self-organizing maps
- In In The 14th Annual Canadian Information Technology Security Symposium
, 2002
"... Abstract – A system is described for applying hierarchical unsupervised neural networks (self organizing feature maps) to the intruder detection problem. Specific emphasis is given to the representation of time and the incremental development of a hierarchy. Preliminary results are given for the DAR ..."
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Cited by 15 (0 self)
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Abstract – A system is described for applying hierarchical unsupervised neural networks (self organizing feature maps) to the intruder detection problem. Specific emphasis is given to the representation of time and the incremental development of a hierarchy. Preliminary results are given
Context Learning with the Self-Organizing Map
, 1997
"... In this paper a Recurrent Self-Organizing Map (RSOM) algorithm is proposed for temporal sequence processing. The RSOM algorithm is close in nature to the Kohonen's Self-Organizing Map, except that in the RSOM context of the temporal sequence is involved both in the best matching unit finding an ..."
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Cited by 19 (6 self)
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In this paper a Recurrent Self-Organizing Map (RSOM) algorithm is proposed for temporal sequence processing. The RSOM algorithm is close in nature to the Kohonen's Self-Organizing Map, except that in the RSOM context of the temporal sequence is involved both in the best matching unit finding
Using Psycho-Acoustic Models and Self-Organizing Maps To Create Hierarchical Structuring of Music by Sound Similarity
, 2002
"... With the advent of large musical archives the need to provide an organization of these archives becomes eminent. While artist-based organizations or title indexes may help in locating a specific piece of music, a more intuitive, genre-based organization is required to allow users to browse an archiv ..."
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Cited by 79 (22 self)
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With the advent of large musical archives the need to provide an organization of these archives becomes eminent. While artist-based organizations or title indexes may help in locating a specific piece of music, a more intuitive, genre-based organization is required to allow users to browse
Results 1 - 10
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668
