Above are papers related to the following paper: [Paper_ID] Paper #50807. 	[Title] Unsupervised Learning with the Soft-Means Algorithm [Abstract] This note describes a useful adaptation of the `peak seeking' regime used in unsupervised learning processes such as competitive learning and `k-means'. The adaptation enables the learning to capture low-order probability effects and thus to more fully capture the probabilistic structure of the training data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104749. 	[Title] Learning Where To Go without Knowing Where That Is: The Acquisition of a Non-reactive Mobot [Abstract] In the path-imitation task, one agent traces out a path through a second agent's sensory field. The second agent then has to reproduce that path exactly, i.e. move through the sequence of locations visited by the first agent. This is a non-trivial behaviour whose acquisition might be expected to involve special-purpose (i.e., strongly biased) learning machinery. However, the present paper shows this is not the case. The behaviour can be acquired using a fairly primitive learning regime provided that the agent's environment can be made to pass through a specific sequence of dynamic states. Please summarise all the given information.

Paper #50807 has the title `Unsupervised Learning with the Soft-Means Algorithm  
` and the abstract `This note describes a useful adaptation of the `peak seeking' regime used in unsupervised learning processes such as competitive learning and `k-means'. The adaptation enables the learning to capture low-order probability effects and thus to more fully capture the probabilistic structure of the training data. 
`.
Question: Which category should Paper #50807 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #50807 has the title `Unsupervised Learning with the Soft-Means Algorithm  
` and the abstract `This note describes a useful adaptation of the `peak seeking' regime used in unsupervised learning processes such as competitive learning and `k-means'. The adaptation enables the learning to capture low-order probability effects and thus to more fully capture the probabilistic structure of the training data. 
`.
Question: Which category should Paper #50807 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: Genetic_Algorithms