Above are papers related to the following paper: [Paper_ID] Paper #131122. 	[Title] The Task Rehearsal Method of Sequential Learning [Abstract] An hypothesis of functional transfer of task knowledge is presented that requires the development of a measure of task relatedness and a method of sequential learning. The task rehearsal method (TRM) is introduced to address the issues of sequential learning, namely retention and transfer of knowledge. TRM is a knowledge based inductive learning system that uses functional domain knowledge as a source of inductive bias. The representations of successfully learned tasks are stored within domain knowledge. Virtual examples generated by domain knowledge are rehearsed in parallel with the each new task using either the standard multiple task learning (MTL) or the MTL neural network methods. The results of experiments conducted on a synthetic domain of seven tasks demonstrate the method's ability to retain and transfer task knowledge. TRM is shown to be effective in developing hypothesis for tasks that suffer from impoverished training sets. Difficulties encountered during sequential learning over the diverse domain reinforce the need for a more robust measure of task relatedness. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6209. 	[Title] Transfer of Learning by Composing Solutions of Elemental Sequential Tasks [Abstract] Although building sophisticated learning agents that operate in complex environments will require learning to perform multiple tasks, most applications of reinforcement learning have focussed on single tasks. In this paper I consider a class of sequential decision tasks (SDTs), called composite sequential decision tasks, formed by temporally concatenating a number of elemental sequential decision tasks. Elemental SDTs cannot be decomposed into simpler SDTs. I consider a learning agent that has to learn to solve a set of elemental and composite SDTs. I assume that the structure of the composite tasks is unknown to the learning agent. The straightforward application of reinforcement learning to multiple tasks requires learning the tasks separately, which can waste computational resources, both memory and time. I present a new learning algorithm and a modular architecture that learns the decomposition of composite SDTs, and achieves transfer of learning by sharing the solutions of elemental SDTs across multiple composite SDTs. The solution of a composite SDT is constructed by computationally inexpensive modifications of the solutions of its constituent elemental SDTs. I provide a proof of one aspect of the learning algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #212930. 	[Title] The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis [Abstract] A distinction between two forms of task knowledge transfer, representational and functional, is reviewed followed by a discussion of MTL, a modified version of the multiple task learning (MTL) neural network method of functional transfer. The MTL method employs a separate learning rate, k , for each task output node k. k varies as a function of a measure of relatedness, R k , between the kth task and the primary task of interest. An MTL network is applied to a diagnostic domain of four levels of coronary artery disease. Results of experiments demonstrate the ability of MTL to develop a predictive model for one level of disease which has superior diagnostic ability over models produced by either single task learning or standard multiple task learning. 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 #131117. 	[Title] Learning Sequential Tasks by Incrementally Adding Higher Orders [Abstract] An incremental, higher-order, non-recurrent network combines two properties found to be useful for learning sequential tasks: higher-order connections and incremental introduction of new units. The network adds higher orders when needed by adding new units that dynamically modify connection weights. Since the new units modify the weights at the next time-step with information from the previous step, temporal tasks can be learned without the use of feedback, thereby greatly simplifying training. Furthermore, a theoretically unlimited number of units can be added to reach into the arbitrarily distant past. Experiments with the Reber grammar have demonstrated speedups of two orders of magnitude over recurrent networks. Please summarise all the given information.

Paper #131122 has the title `The Task Rehearsal Method of Sequential Learning  
` and the abstract `An hypothesis of functional transfer of task knowledge is presented that requires the development of a measure of task relatedness and a method of sequential learning. The task rehearsal method (TRM) is introduced to address the issues of sequential learning, namely retention and transfer of knowledge. TRM is a knowledge based inductive learning system that uses functional domain knowledge as a source of inductive bias. The representations of successfully learned tasks are stored within domain knowledge. Virtual examples generated by domain knowledge are rehearsed in parallel with the each new task using either the standard multiple task learning (MTL) or the MTL neural network methods. The results of experiments conducted on a synthetic domain of seven tasks demonstrate the method's ability to retain and transfer task knowledge. TRM is shown to be effective in developing hypothesis for tasks that suffer from impoverished training sets. Difficulties encountered during sequential learning over the diverse domain reinforce the need for a more robust measure of task relatedness. 
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Question: Which category should Paper #131122 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #131122 has the title `The Task Rehearsal Method of Sequential Learning  
` and the abstract `An hypothesis of functional transfer of task knowledge is presented that requires the development of a measure of task relatedness and a method of sequential learning. The task rehearsal method (TRM) is introduced to address the issues of sequential learning, namely retention and transfer of knowledge. TRM is a knowledge based inductive learning system that uses functional domain knowledge as a source of inductive bias. The representations of successfully learned tasks are stored within domain knowledge. Virtual examples generated by domain knowledge are rehearsed in parallel with the each new task using either the standard multiple task learning (MTL) or the MTL neural network methods. The results of experiments conducted on a synthetic domain of seven tasks demonstrate the method's ability to retain and transfer task knowledge. TRM is shown to be effective in developing hypothesis for tasks that suffer from impoverished training sets. Difficulties encountered during sequential learning over the diverse domain reinforce the need for a more robust measure of task relatedness. 
`.
Question: Which category should Paper #131122 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