Above are papers related to the following paper: [Paper_ID] Paper #159085. 	[Title] Some Comments on the Information Stored in Sparse Distributed Memory [Abstract] Report R95:11 ISRN : SICS-R--95/11-SE ISSN : 0283-3638 Abstract We consider a sparse distributed memory with randomly chosen hard locations, in which an unknown number T of random data vectors have been stored. A method is given to estimate T from the content of the memory with high accuracy. In fact, our estimate is unbiased, the coefficient of variation being roughly inversely proportional to p MU , where M is the number of hard locations in the memory and U the length of data, so the accuracy can be made arbitrarily high by making the memory big enough. A consequence of this is that the good reading methods in [5] and [6] can be used without any need for the special extra location introduced there. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #159084. 	[Title] Best Probability of Activation and Performance Comparisons for Several Designs of Sparse Distributed Memory [Abstract] Report R95:09 ISRN : SICS-R-95/09-SE ISSN : 0283-3638 Abstract The optimal probability of activation and the corresponding performance is studied for three designs of Sparse Distributed Memory, namely, Kanerva's original design, Jaeckel's selected-coordinates design and Karlsson's modifi - cation of Jaeckel's design. We will assume that the hard locations (in Karlsson's case, the masks), the storage addresses and the stored data are randomly chosen, and we will consider different levels of random noise in the reading address. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #241821. 	[Title] Convergence and new operations in SDM new method for converging in the SDM memory, utilizing [Abstract] Report R95:13 ISRN : SICS-R--95/13-SE ISSN : 0283-3638 Abstract Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152711. 	[Title] Capacity of SDM [Abstract] Report R95:12 ISRN : SICS-R--95/12-SE ISSN : 0283-3638 Abstract A more efficient way of reading the SDM memory is presented. This is accomplished by using implicit information, hitherto not utilized, to find the information-carrying units and thus removing unnecessary noise when reading the memory. Please summarise all the given information.

Paper #159085 has the title `Some Comments on the Information Stored in Sparse Distributed Memory  
` and the abstract `Report R95:11 ISRN : SICS-R--95/11-SE ISSN : 0283-3638 Abstract We consider a sparse distributed memory with randomly chosen hard locations, in which an unknown number T of random data vectors have been stored. A method is given to estimate T from the content of the memory with high accuracy. In fact, our estimate is unbiased, the coefficient of variation being roughly inversely proportional to p MU , where M is the number of hard locations in the memory and U the length of data, so the accuracy can be made arbitrarily high by making the memory big enough. A consequence of this is that the good reading methods in [5] and [6] can be used without any need for the special extra location introduced there. 
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Question: Which category should Paper #159085 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #159085 has the title `Some Comments on the Information Stored in Sparse Distributed Memory  
` and the abstract `Report R95:11 ISRN : SICS-R--95/11-SE ISSN : 0283-3638 Abstract We consider a sparse distributed memory with randomly chosen hard locations, in which an unknown number T of random data vectors have been stored. A method is given to estimate T from the content of the memory with high accuracy. In fact, our estimate is unbiased, the coefficient of variation being roughly inversely proportional to p MU , where M is the number of hard locations in the memory and U the length of data, so the accuracy can be made arbitrarily high by making the memory big enough. A consequence of this is that the good reading methods in [5] and [6] can be used without any need for the special extra location introduced there. 
`.
Question: Which category should Paper #159085 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