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 #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 #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.

Above are papers related to the following paper: [Paper_ID] Paper #1135082. 	[Title] Incremental Class Learning approach and its application to Handwritten Digit Recognition [Abstract] Incremental Class Learning (ICL) provides a feasible framework for the development of scalable learning systems. Instead of learning a complex problem at once, ICL focuses on learning subproblems incrementally, one at a time | using the results of prior learning for subsequent learning | and then combining the solutions in an appropriate manner. With respect to multi-class classification problems, the ICL approach presented in this paper can be summarized as follows. Initially the system focuses on one category. After it learns this category, it tries to identify a compact subset of features (nodes) in the hidden layers, that are crucial for the recognition of this category. The system then freezes these crucial nodes (features) by fixing their incoming weights. As a result, these features cannot be obliterated in subsequent learning. These frozen features are available during subsequent learning and can serve as parts of weight structures build to recognize other categories. As more categories are learned, the set of features gradually stabilizes and learning a new category requires less effort. Eventually, learning a new category may only involve combining existing features in an appropriate manner. The approach promotes the sharing of learned features among a number of categories and also alleviates the well-known catastrophic interference problem. We present results of applying the ICL approach to the Handwritten Digit Recognition problem, based on a spatio-temporal representation of patterns. Please summarise all the given information.

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 #14549. 	[Title] Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version) [Abstract] Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause more or less radical changes in the concepts. Incremental concept learning in such domains requires the ability to recognize and adapt to such changes. This paper presents a solution for incremental learning tasks where the domain provides explicit clues as to the current context (e.g., attributes with characteristic values). We present a general two-level learning model, and its realization in a system named MetaL(B), that can learn to detect certain types of contextual clues, and can react accordingly when a context change is suspected. The model consists of a base level learner that performs the regular on-line learning and classification task, and a meta-learner that identifies potential contextual clues. Context learning and detection occur during regular on-line learning, without separate training phases for context recognition. Experiments with synthetic domains as well as a `real-world' problem show that MetaL(B) is robust in a variety of dimensions and produces substantial improvement over simple object-level learning in situations with changing contexts. Please summarise all the given information.

Paper #54844 has the title `Learning Model Bias minimum number of examples requred to learn a single task, and O(a
` and the 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. 
`.
Question: Which category should Paper #54844 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #54844 has the title `Learning Model Bias minimum number of examples requred to learn a single task, and O(a
` and the 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. 
`.
Question: Which category should Paper #54844 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