Above are papers related to the following paper: [Paper_ID] Paper #1125258. 	[Title] Automated Highway System [Abstract] ALVINN (Autonomous Land Vehicle in a Neural Net) is a Backpropagation trained neural network which is capable of autonomously steering a vehicle in road and highway environments. Although ALVINN is fairly robust, one of the problems with it has been the time it takes to train. As the vehicle is capable of on-line learning, the driver has to drive the car for about 2 minutes before the network is capable of autonomous operation. One reason for this is the use of Backprop. In this report, we describe the original ALVINN system, and then look at three alternative training methods - Quickprop, Cascade Correlation, and Cascade 2. We then run a series of trials using Quickprop, Cascade Correlation and Cascade2, and compare them to a BackProp baseline. Finally, a hidden unit analysis is performed to determine what the network is learning. Applying Advanced Learning Algorithms to ALVINN Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #114966. 	[Title] MANIAC: A Next Generation Neurally Based Autonomous Road Follower [Abstract] The use of artificial neural networks in the domain of autonomous vehicle navigation has produced promising results. ALVINN [Pomerleau, 1991] has shown that a neural system can drive a vehicle reliably and safely on many different types of roads, ranging from paved paths to interstate highways. Even with these impressive results, several areas within the neural paradigm for autonomous road following still need to be addressed. These include transparent navigation between roads of different type, simultaneous use of different sensors, and generalization to road types which the neural system has never seen. The system presented here addresses these issue with a modular neural architecture which uses pre-trained ALVINN networks and a connectionist superstructure to robustly drive on many dif ferent types of roads. Please summarise all the given information.

Paper #1125258 has the title `Automated Highway System  
` and the abstract `ALVINN (Autonomous Land Vehicle in a Neural Net) is a Backpropagation trained neural network which is capable of autonomously steering a vehicle in road and highway environments. Although ALVINN is fairly robust, one of the problems with it has been the time it takes to train. As the vehicle is capable of on-line learning, the driver has to drive the car for about 2 minutes before the network is capable of autonomous operation. One reason for this is the use of Backprop. In this report, we describe the original ALVINN system, and then look at three alternative training methods - Quickprop, Cascade Correlation, and Cascade 2. We then run a series of trials using Quickprop, Cascade Correlation and Cascade2, and compare them to a BackProp baseline. Finally, a hidden unit analysis is performed to determine what the network is learning. Applying Advanced Learning Algorithms to ALVINN 
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Question: Which category should Paper #1125258 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1125258 has the title `Automated Highway System  
` and the abstract `ALVINN (Autonomous Land Vehicle in a Neural Net) is a Backpropagation trained neural network which is capable of autonomously steering a vehicle in road and highway environments. Although ALVINN is fairly robust, one of the problems with it has been the time it takes to train. As the vehicle is capable of on-line learning, the driver has to drive the car for about 2 minutes before the network is capable of autonomous operation. One reason for this is the use of Backprop. In this report, we describe the original ALVINN system, and then look at three alternative training methods - Quickprop, Cascade Correlation, and Cascade 2. We then run a series of trials using Quickprop, Cascade Correlation and Cascade2, and compare them to a BackProp baseline. Finally, a hidden unit analysis is performed to determine what the network is learning. Applying Advanced Learning Algorithms to ALVINN 
`.
Question: Which category should Paper #1125258 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