LVQPAK: A software package for the correct application of Learning Vector Quantization algorithms

Kohonen, Teuvo; Kangas, Jari; Laaksonen, Jorma; Torkkola, Kari

doi:10.1109/ijcnn.1992.287101

Cited by 105 publications

(70 citation statements)

References 2 publications

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“…However, little has been published about LVQ algorithmic settings beyond 1) the general hierarchy of 0 ≤ ( ) ≤ ( ) ≤ 1 for relevance-based LVQ methods [54], 2) specific guidelines for specific applications, e.g. [55], [56], and 3) learning rate convergence methods, e.g. [55].…”

Section: Grlvqi Algorithmic Settingsmentioning

confidence: 99%

An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting

Bihl¹,

Temple²,

Bauer³

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Section: Grlvqi Algorithmic Settingsmentioning

confidence: 99%

An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting

Bihl¹,

Temple²,

Bauer³

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…Prototype-based supervised algorithms. Learning Vector Quantization (LVQ) is one of the well-known nearest prototype learning algorithms [25]. LVQ can be considered to be a supervised clustering algorithm, in which each weight vector can be interpreted as a cluster center.…”

Section: Background and Related Workmentioning

confidence: 99%

Creating Evolving User Behavior Profiles Automatically

Iglesias

Angelov

Ledezma

et al. 2012

IEEE Trans. Knowl. Data Eng.

114

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Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behavior profile of a computer user is presented. In this case, a computer user behavior is represented as the sequence of the commands she/he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find out a profile that defines its behavior. Also, because a user profile is not necessarily fixed but rather it evolves/changes, we propose an evolving method to keep up to date the created profiles using an Evolving Systems approach. In this paper, we combine the evolving classifier with a trie-based user profiling to obtain a powerful self-learning online scheme. We also develop further the recursive formula of the potential of a data point to become a cluster center using cosine distance, which is provided in the Appendix. The novel approach proposed in this paper can be applicable to any problem of dynamic/evolving user behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams.

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“…Learning Vector Quantization (LVQ) was developed by Kohonen et al [6]. The idea of this algorithm is to find a natural grouping in a set of data.…”

Section: Learning Vector Quantizationmentioning

confidence: 99%

“…In the case of primary sequence as pre-processing a binary input must be used, otherwise the big difference in input values (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) would have a great influence on the result. We have therefore used a 95 nodes input layer where each sliding window index is represented by a 5 nodes binary signal.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Comparing approaches to predict transmembrane domains in protein sequences

Davidsson

Hagelbäck²,

Svensson

2005

Proceedings of the 2005 ACM Symposium on Applied Computing

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There are today several systems for predicting transmembrane domains in membrane protein sequences. As they are based on different classifiers as well as different pre-and post-processing techniques, it is very difficult to evaluate the performance of the particular classifier used. We have developed a system called MemMiC for predicting transmembrane domains in protein sequences with the possibility to choose between different approaches to pre-and post-processing as well as different classifiers. Therefore it is possible to compare the performance of each classifier in a certain environment as well as the different approaches to pre-and post-processing. We have demonstrated the usefulness of MemMiC in a set of experiments, which shows, e.g., that the performance of a classifier is very dependent on which pre-and post-processing techniques are used.

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LVQPAK: A software package for the correct application of Learning Vector Quantization algorithms

Cited by 105 publications

References 2 publications

An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting

An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting

Creating Evolving User Behavior Profiles Automatically

Comparing approaches to predict transmembrane domains in protein sequences

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