Above are papers related to the following paper: [Paper_ID] Paper #1123493. 	[Title] Assessment of candidate pfsa models induced from symbol datasets [Abstract] The induction of the optimal finite state machine explanation from symbol strings is known to be at least NP-complete. However, satisfactory approximately optimal explanations may be found by the use of Evolutionary Programming. It has been shown that an information theoretic measure of finite state machine explanations can be used as the fitness function required for the evaluation of candidate explanations during the search for a near-optimal explanation. It is not obvious from the measure which class of explanation will be favoured over others during the search. By empirical studies it is possible to gain some insight into the dimensions the measure is optimising. In general, for probabilistic finite state machines, explanations assessed by a minimum message length estimator with the minimum number of transitions will be favoured over other explanations. The information measure will also favour explanations with uneven distributions of frequencies on transitions from a node suggesting that repeated sequences in symbol strings will be preferred as an explanation. Approximate bounds for acceptance of explanations and the length of string required for induction to be successful are also derived by considerations of the simplest possible and random explanations and their information measure. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #458439. 	[Title] PFSA Modelling of Behavioural Sequences by Evolutionary Programming Rockhampton, Queensland. (1994) "PFSA Modelling of Behavioural [Abstract] Behavioural observations can often be described as a sequence of symbols drawn from a finite alphabet. However the inductive inference of such strings by any automated technique to produce models of the data is a nontrivial task. This paper considers modelling of behavioural data using probabilistic finite state automata (PFSAs). There are a number of information-theoretic techniques for evaluating possible hypotheses. The measure used in this paper is the Minimum Message Length (MML) of Wallace. Although attempts have been made to construct PFSA models by incremental addition of substrings using heuristic rules and the MML to give the lowest information cost, the resultant models cannot be shown to be globally optimal. Fogel's Evolutionary Programming can produce globally optimal PFSA models by evolving data structures of arbitrary complexity without the requirement to encode the PFSA into binary strings as in Genetic Algorithms. However, evaluation of PFSAs during the evolution process by the MML of the PFSA alone is not possible since there will be symbols which cannot be consumed by a partially correct solution. It is suggested that the addition of a "can't consume'' symbol to the symbol alphabet obviates this difficulty. The addition of this null symbol to the alphabet also permits the evolution of explanatory models which need not explain all of the data, a useful property to avoid overfitting noisy data. Results are given for a test set for which the optimal pfsa model is known and for a set of eye glance data derived from an instrument panel simulator. Please summarise all the given information.

Paper #1123493 has the title `Assessment of candidate pfsa models induced from symbol datasets  
` and the abstract `The induction of the optimal finite state machine explanation from symbol strings is known to be at least NP-complete. However, satisfactory approximately optimal explanations may be found by the use of Evolutionary Programming. It has been shown that an information theoretic measure of finite state machine explanations can be used as the fitness function required for the evaluation of candidate explanations during the search for a near-optimal explanation. It is not obvious from the measure which class of explanation will be favoured over others during the search. By empirical studies it is possible to gain some insight into the dimensions the measure is optimising. In general, for probabilistic finite state machines, explanations assessed by a minimum message length estimator with the minimum number of transitions will be favoured over other explanations. The information measure will also favour explanations with uneven distributions of frequencies on transitions from a node suggesting that repeated sequences in symbol strings will be preferred as an explanation. Approximate bounds for acceptance of explanations and the length of string required for induction to be successful are also derived by considerations of the simplest possible and random explanations and their information measure. 
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Question: Which category should Paper #1123493 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1123493 has the title `Assessment of candidate pfsa models induced from symbol datasets  
` and the abstract `The induction of the optimal finite state machine explanation from symbol strings is known to be at least NP-complete. However, satisfactory approximately optimal explanations may be found by the use of Evolutionary Programming. It has been shown that an information theoretic measure of finite state machine explanations can be used as the fitness function required for the evaluation of candidate explanations during the search for a near-optimal explanation. It is not obvious from the measure which class of explanation will be favoured over others during the search. By empirical studies it is possible to gain some insight into the dimensions the measure is optimising. In general, for probabilistic finite state machines, explanations assessed by a minimum message length estimator with the minimum number of transitions will be favoured over other explanations. The information measure will also favour explanations with uneven distributions of frequencies on transitions from a node suggesting that repeated sequences in symbol strings will be preferred as an explanation. Approximate bounds for acceptance of explanations and the length of string required for induction to be successful are also derived by considerations of the simplest possible and random explanations and their information measure. 
`.
Question: Which category should Paper #1123493 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