Above are papers related to the following paper: [Paper_ID] Paper #1119671. 	[Title] The Canonical Distortion Measure in Feature Space and 1-NN Classification [Abstract] We prove that the Canonical Distortion Measure (CDM) [2, 3] is the optimal distance measure to use for 1 nearest-neighbour (1-NN) classification, and show that it reduces to squared Euclidean distance in feature space for function classes that can be expressed as linear combinations of a fixed set of features. PAC-like bounds are given on the sample-complexity required to learn the CDM. An experiment is presented in which a neural network CDM was learnt for a Japanese OCR environ ment and then used to do 1-NN classification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7047. 	[Title] Learning One More Thing [Abstract] Most research on machine learning has focused on scenarios in which a learner faces a single, isolated learning task. The lifelong learning framework assumes that the learner encounters a multitude of related learning tasks over its lifetime, providing the opportunity for the transfer of knowledge among these. This paper studies lifelong learning in the context of binary classification. It presents the invariance approach, in which knowledge is transferred via a learned model of the invariances of the domain. Results on learning to recognize objects from color images demonstrate superior generalization capabilities if invariances are learned and used to bias subsequent learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2663. 	[Title] The Canonical Distortion Measure for Vector Quantization and Function Approximation [Abstract] To measure the quality of a set of vector quantization points a means of measuring the distance between a random point and its quantization is required. Common metrics such as the Hamming and Euclidean metrics, while mathematically simple, are inappropriate for comparing natural signals such as speech or images. In this paper it is shown how an environment of functions on an input space X induces a canonical distortion measure (CDM) on X. The depiction canonical is justified because it is shown that optimizing the reconstruction error of X with respect to the CDM gives rise to optimal piecewise constant approximations of the functions in the environment. The CDM is calculated in closed form for several different function classes. An algorithm for training neural networks to implement the CDM is presented along with some en couraging experimental results. Please summarise all the given information.

Paper #1119671 has the title `The Canonical Distortion Measure in Feature Space and 1-NN Classification  
` and the abstract `We prove that the Canonical Distortion Measure (CDM) [2, 3] is the optimal distance measure to use for 1 nearest-neighbour (1-NN) classification, and show that it reduces to squared Euclidean distance in feature space for function classes that can be expressed as linear combinations of a fixed set of features. PAC-like bounds are given on the sample-complexity required to learn the CDM. An experiment is presented in which a neural network CDM was learnt for a Japanese OCR environ ment and then used to do 1-NN classification.
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Question: Which category should Paper #1119671 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1119671 has the title `The Canonical Distortion Measure in Feature Space and 1-NN Classification  
` and the abstract `We prove that the Canonical Distortion Measure (CDM) [2, 3] is the optimal distance measure to use for 1 nearest-neighbour (1-NN) classification, and show that it reduces to squared Euclidean distance in feature space for function classes that can be expressed as linear combinations of a fixed set of features. PAC-like bounds are given on the sample-complexity required to learn the CDM. An experiment is presented in which a neural network CDM was learnt for a Japanese OCR environ ment and then used to do 1-NN classification.
`.
Question: Which category should Paper #1119671 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