Above are papers related to the following paper: [Paper_ID] Paper #1122304. 	[Title] The Gamma MLP Using Multiple Temporal Resolutions for Improved Classification [Abstract] We have previously introduced the Gamma MLP which is defined as an MLP with the usual synaptic weights replaced by gamma filters and associated gain terms throughout all layers. In this paper we apply the Gamma MLP to a larger scale speech phoneme recognition problem, analyze the operation of the network, and investigate why the Gamma MLP can perform better than alternatives. The Gamma MLP is capable of employing multiple temporal resolutions (the temporal resolution is defined here, as per de Vries and Principe, as the number of parameters of freedom (i.e. the number of tap variables) per unit of time in the gamma memory this is equal to the gamma memory parameter as detailed in the paper). Multiple temporal resolutions may be advantageous for certain problems, e.g. different resolutions may be optimal for extracting different features from the input data. For the problem in this paper, the Gamma MLP is observed to use a large range of temporal resolutions. In comparison, TDNN networks typically use only a single temporal resolution. Further motivation for the Gamma MLP is related to the curse of dimensionality and the ability of the Gamma MLP to trade off temporal resolution for memory depth, and therefore increase memory depth without increasing the dimensionality of the network. The IIR MLP is a more general version of the Gamma MLP however the IIR MLP performs poorly for the problem in this paper. Investigation suggests that the error surface of the Gamma MLP is more suitable for gradient descent training than the error surface of the IIR MLP. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #226698. 	[Title] The Gamma MLP for Speech Phoneme Recognition [Abstract] We define a Gamma multi-layer perceptron (MLP) as an MLP with the usual synaptic weights replaced by gamma filters (as proposed by de Vries and Principe (de Vries & Principe 1992)) and associated gain terms throughout all layers. We derive gradient descent update equations and apply the model to the recognition of speech phonemes. We find that both the inclusion of gamma filters in all layers, and the inclusion of synaptic gains, improves the performance of the Gamma MLP. We compare the Gamma MLP with TDNN, Back-Tsoi FIR MLP, and Back-Tsoi IIR MLP architectures, and a local approximation scheme. We find that the Gamma MLP results in a substantial reduction in error rates. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112767. 	[Title] Alternative Discrete-Time Operators and Their Application to Nonlinear Models [Abstract]  Please summarise all the given information.

Paper #1122304 has the title `The Gamma MLP Using Multiple Temporal Resolutions for Improved Classification  
` and the abstract `We have previously introduced the Gamma MLP which is defined as an MLP with the usual synaptic weights replaced by gamma filters and associated gain terms throughout all layers. In this paper we apply the Gamma MLP to a larger scale speech phoneme recognition problem, analyze the operation of the network, and investigate why the Gamma MLP can perform better than alternatives. The Gamma MLP is capable of employing multiple temporal resolutions (the temporal resolution is defined here, as per de Vries and Principe, as the number of parameters of freedom (i.e. the number of tap variables) per unit of time in the gamma memory this is equal to the gamma memory parameter as detailed in the paper). Multiple temporal resolutions may be advantageous for certain problems, e.g. different resolutions may be optimal for extracting different features from the input data. For the problem in this paper, the Gamma MLP is observed to use a large range of temporal resolutions. In comparison, TDNN networks typically use only a single temporal resolution. Further motivation for the Gamma MLP is related to the curse of dimensionality and the ability of the Gamma MLP to trade off temporal resolution for memory depth, and therefore increase memory depth without increasing the dimensionality of the network. The IIR MLP is a more general version of the Gamma MLP however the IIR MLP performs poorly for the problem in this paper. Investigation suggests that the error surface of the Gamma MLP is more suitable for gradient descent training than the error surface of the IIR MLP. 
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Question: Which category should Paper #1122304 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1122304 has the title `The Gamma MLP Using Multiple Temporal Resolutions for Improved Classification  
` and the abstract `We have previously introduced the Gamma MLP which is defined as an MLP with the usual synaptic weights replaced by gamma filters and associated gain terms throughout all layers. In this paper we apply the Gamma MLP to a larger scale speech phoneme recognition problem, analyze the operation of the network, and investigate why the Gamma MLP can perform better than alternatives. The Gamma MLP is capable of employing multiple temporal resolutions (the temporal resolution is defined here, as per de Vries and Principe, as the number of parameters of freedom (i.e. the number of tap variables) per unit of time in the gamma memory this is equal to the gamma memory parameter as detailed in the paper). Multiple temporal resolutions may be advantageous for certain problems, e.g. different resolutions may be optimal for extracting different features from the input data. For the problem in this paper, the Gamma MLP is observed to use a large range of temporal resolutions. In comparison, TDNN networks typically use only a single temporal resolution. Further motivation for the Gamma MLP is related to the curse of dimensionality and the ability of the Gamma MLP to trade off temporal resolution for memory depth, and therefore increase memory depth without increasing the dimensionality of the network. The IIR MLP is a more general version of the Gamma MLP however the IIR MLP performs poorly for the problem in this paper. Investigation suggests that the error surface of the Gamma MLP is more suitable for gradient descent training than the error surface of the IIR MLP. 
`.
Question: Which category should Paper #1122304 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks