Above are papers related to the following paper: [Paper_ID] Paper #1109566. 	[Title] Classification Using -Machines and Constructive Function Approximation [Abstract] The classification algorithm CLEF combines a version of a linear machine known as a - machine with a non-linear function approxima-tor that constructs its own features. The algorithm finds non-linear decision boundaries by constructing features that are needed to learn the necessary discriminant functions. The CLEF algorithm is proven to separate all consistently labelled training instances, even when they are not linearly separable in the input variables. The algorithm is illustrated on a variety of tasks, showing an improvement over C4.5, a state-of-art de cision tree learning algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #118873. 	[Title] Constructive Neural Network Learning Algorithms for Multi-Category Real-Valued Pattern Classification [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #987188. 	[Title] A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers [Abstract] This paper explores some algorithms for automatic quantization of real-valued datasets using thermometer codes for pattern classification applications. Experimental results indicate that a relatively simple randomized thermometer code generation technique can result in quantized datasets that when used to train simple perceptrons, can yield generalization on test data that is substantially better than that obtained with their unquantized counterparts. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115959. 	[Title] Pruning Strategies for the MTiling Constructive Learning Algorithm [Abstract] We present a framework for incorporating pruning strategies in the MTiling constructive neural network learning algorithm. Pruning involves elimination of redundant elements (connection weights or neurons) from a network and is of considerable practical interest. We describe three elementary sensitivity based strategies for pruning neurons. Experimental results demonstrate a moderate to significant reduction in the network size without compromising the network's generalization performance. Please summarise all the given information.

Paper #1109566 has the title `Classification Using -Machines and Constructive Function Approximation  
` and the abstract `The classification algorithm CLEF combines a version of a linear machine known as a - machine with a non-linear function approxima-tor that constructs its own features. The algorithm finds non-linear decision boundaries by constructing features that are needed to learn the necessary discriminant functions. The CLEF algorithm is proven to separate all consistently labelled training instances, even when they are not linearly separable in the input variables. The algorithm is illustrated on a variety of tasks, showing an improvement over C4.5, a state-of-art de cision tree learning algorithm.
`.
Question: Which category should Paper #1109566 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1109566 has the title `Classification Using -Machines and Constructive Function Approximation  
` and the abstract `The classification algorithm CLEF combines a version of a linear machine known as a - machine with a non-linear function approxima-tor that constructs its own features. The algorithm finds non-linear decision boundaries by constructing features that are needed to learn the necessary discriminant functions. The CLEF algorithm is proven to separate all consistently labelled training instances, even when they are not linearly separable in the input variables. The algorithm is illustrated on a variety of tasks, showing an improvement over C4.5, a state-of-art de cision tree learning algorithm.
`.
Question: Which category should Paper #1109566 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: Theory