Above are papers related to the following paper: [Paper_ID] Paper #1104946. 	[Title] Theoretical Models of Learning to Learn  Editor: [Abstract] A Machine can only learn if it is biased in some way. Typically the bias is supplied by hand, for example through the choice of an appropriate set of features. However, if the learning machine is embedded within an environment of related tasks, then it can learn its own bias by learning sufficiently many tasks from the environment [4, 6]. In this paper two models of bias learning (or equivalently, learning to learn) are introduced and the main theoretical results presented. The first model is a PAC-type model based on empirical process theory, while the second is a hierarchical Bayes model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #54844. 	[Title] Learning Model Bias minimum number of examples requred to learn a single task, and O(a [Abstract] In this paper the problem of learning appropriate domain-specific bias is addressed. It is shown that this can be achieved by learning many related tasks from the same domain, and a theorem is given bounding the number tasks that must be learnt. A corollary of the theorem is that if the tasks are known to possess a common internal representation or preprocessing then the number of examples required per task for good generalisation when learning n tasks simultaneously scales like O(a + b tive support for the theoretical results is reported. 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.

Paper #1104946 has the title `Theoretical Models of Learning to Learn  Editor:  
` and the abstract `A Machine can only learn if it is biased in some way. Typically the bias is supplied by hand, for example through the choice of an appropriate set of features. However, if the learning machine is embedded within an environment of related tasks, then it can learn its own bias by learning sufficiently many tasks from the environment [4, 6]. In this paper two models of bias learning (or equivalently, learning to learn) are introduced and the main theoretical results presented. The first model is a PAC-type model based on empirical process theory, while the second is a hierarchical Bayes model. 
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Question: Which category should Paper #1104946 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1104946 has the title `Theoretical Models of Learning to Learn  Editor:  
` and the abstract `A Machine can only learn if it is biased in some way. Typically the bias is supplied by hand, for example through the choice of an appropriate set of features. However, if the learning machine is embedded within an environment of related tasks, then it can learn its own bias by learning sufficiently many tasks from the environment [4, 6]. In this paper two models of bias learning (or equivalently, learning to learn) are introduced and the main theoretical results presented. The first model is a PAC-type model based on empirical process theory, while the second is a hierarchical Bayes model. 
`.
Question: Which category should Paper #1104946 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