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19
Recurrent SOM with Local Linear Models in Time Series Prediction
 In 6th European Symposium on Artificial Neural Networks
, 1998
"... Recurrent SelfOrganizing Map (RSOM) is studied in three different time series prediction cases. RSOM is used to cluster the series into local data sets, for which corresponding local linear models are estimated. RSOM includes recurrent difference vector in each unit which allows storing context fro ..."
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Recurrent SelfOrganizing Map (RSOM) is studied in three different time series prediction cases. RSOM is used to cluster the series into local data sets, for which corresponding local linear models are estimated. RSOM includes recurrent difference vector in each unit which allows storing context from the past input vectors. Multilayer perceptron (MLP) network and autoregressive (AR) model are used to compare the prediction results. In studied cases RSOM shows promising results.
Time Series Prediction Using Recurrent SOM with Local Linear Models
 INTERNATIONAL JOURNAL OF KNOWLEDGEBASED INTELLIGENT ENGINEERING SYSTEMS
, 1997
"... A newly proposed Recurrent SelfOrganizing Map (RSOM) is studied in time series prediction. In this approach RSOM is used to cluster the data to local data sets and local linear models corresponding each of the map units are then estimated based on the local data sets. A traditional way of clusterin ..."
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Cited by 26 (1 self)
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A newly proposed Recurrent SelfOrganizing Map (RSOM) is studied in time series prediction. In this approach RSOM is used to cluster the data to local data sets and local linear models corresponding each of the map units are then estimated based on the local data sets. A traditional way of clustering the data is to use a windowing technique to split it to input vectors of certain length. In this procedure, the temporal context between the consecutive vectors is lost. In RSOM the map units keep track of the past input vectors with a recurrent dioeerence vector in each unit. The recurrent structure allows the map to store information concerning the change in the magnitude and direction of the input vector. RSOM can thus be used to cluster the temporal context in the time series. This allows a dioeerent local model to be selected based on the context and the current input vector of the model. The studied cases show promising results.
Context Quantization and Contextual SelfOrganizing Maps
 In: Proc. Int. Joint Conf. on Neural Networks, vol.5
, 2000
"... Vector quantization consists in nding a discrete approximation of a continuous input. One of the most popular neural algorithms related to vector quantization is the, so called, Kohonen map. In this paper we generalize vector quantization to temporal data, introducing context quantization. We propos ..."
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Cited by 15 (0 self)
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Vector quantization consists in nding a discrete approximation of a continuous input. One of the most popular neural algorithms related to vector quantization is the, so called, Kohonen map. In this paper we generalize vector quantization to temporal data, introducing context quantization. We propose a recurrent network inspired by the Kohonen map, the Contextual SelfOrganizing Map, that develops nearoptimal representations of context. We demonstrate quantitatively that this algorithm shows better performance than the other neural methods proposed so far. 1. Introduction The temporal context present in sequential data is crucial for sequence processing and prediction of future events. An element of a sequence is contextdependent when it cannot be predicted from only one previous element, but also needs additional information provided by the preceding inputs [13]. Most neural techniques for sequence learning are based on recurrent networks, where the context is represented by the ...
Temporal Sequence Processing using Recurrent SOM
 In Proceedings of the 2nd International Conference on KnowledgeBased Intelligent Engineering Systems
, 1998
"... Recurrent SelfOrganizing Map (RSOM) is studied in temporal sequence processing. RSOM includes a recurrent difference vector in each unit of the map, which allows storing temporal context from consecutive input vectors fed to the map. RSOM is a modification of the Temporal Kohonen Map (TKM). It is s ..."
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Recurrent SelfOrganizing Map (RSOM) is studied in temporal sequence processing. RSOM includes a recurrent difference vector in each unit of the map, which allows storing temporal context from consecutive input vectors fed to the map. RSOM is a modification of the Temporal Kohonen Map (TKM). It is shown that RSOM learns a correct mapping from temporal sequences of a simple synthetic data, while TKM fails to learn this mapping. In addition, two case studies are presented, in which RSOM is applied to EEG based epileptic activity detection and to time series prediction with local models. Results suggest that RSOM can be efficiently used in temporal sequence processing.
Visualisation of treestructured data through generative probabilistic modelling, in this volume
"... We present a generative probabilistic model for the topographic mapping of tree structured data. The model is formulated as constrained mixture of hidden Markov tree models. A natural measure of likelihood arises as a cost function that guides the model fitting. We compare our approach with an exist ..."
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Cited by 5 (1 self)
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We present a generative probabilistic model for the topographic mapping of tree structured data. The model is formulated as constrained mixture of hidden Markov tree models. A natural measure of likelihood arises as a cost function that guides the model fitting. We compare our approach with an existing neuralbased methodology for constructing topographic maps of directed acyclic graphs. We argue that the probabilistic nature of our model brings several advantages, such as principled interpretation of the visualisation plots. 1
Temporal Kohonen map and recurrent selforganizing map: analytical and experimental comparison. Neural Processing Letters
 Neural Processing Letters
, 2001
"... Abstract. This paper compares two SelfOrganizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent SelfOrganizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal contex ..."
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Abstract. This paper compares two SelfOrganizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent SelfOrganizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a signi¢cant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments. Key words: convergence analysis, selforganizing maps, temporal sequence processing. 1.
Neural Network Methods In Analysing And Modelling Time Varying Processes
, 2003
"... Teknillinen korkeakoulu Sähkö ja tietoliikennetekniikan osasto Laskennallisen tekniikan laboratorio Distribution: ..."
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Teknillinen korkeakoulu Sähkö ja tietoliikennetekniikan osasto Laskennallisen tekniikan laboratorio Distribution:
A recurrent selforganizing map for temporal sequence processing
 in: Proceedings of Fourth International Conference in Recent Advances in Soft Computing (RASC2002
, 2002
"... Abstract. We present a novel approach to unsupervised temporal sequence processing in the form of an unsupervised, recurrent neural network based on a selforganizing map (SOM). A standard SOM clusters each input vector irrespective of context, whereas the recurrent SOM presented here clusters each i ..."
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Abstract. We present a novel approach to unsupervised temporal sequence processing in the form of an unsupervised, recurrent neural network based on a selforganizing map (SOM). A standard SOM clusters each input vector irrespective of context, whereas the recurrent SOM presented here clusters each input based on an input vector and a context vector. The latter acts as a recurrent conduit feeding back a 2D representation of the previous winning neuron. This recurrency allows the network to operate on temporal sequence processing tasks. The network has been applied to the difficult natural language processing problem of position variant recognition, e.g. recognising a noun phrase regardless of its position within a sentence. 1
Selforganizing maps in sequence processing
, 2002
"... Teknillinen korkeakoulu Sähkö ja tietoliikennetekniikan osasto Laskennallisen tekniikan laboratorio Distribution: ..."
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Cited by 3 (0 self)
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Teknillinen korkeakoulu Sähkö ja tietoliikennetekniikan osasto Laskennallisen tekniikan laboratorio Distribution: