Above are papers related to the following paper: [Paper_ID] Paper #711527. 	[Title] APPLICATION OF ESOP MINIMIZATION IN MACHINE LEARNING AND KNOWLEDGE DISCOVERY [Abstract] This paper presents a new application of an Exclusive-Sum-Of-Products (ESOP) minimizer EXORCISM-MV-2: to Machine Learning, and particularly, in Pattern Theory. An analysis of various logic synthesis programs has been conducted at Wright Laboratory for machine learning applications. Creating a robust and efficient Boolean minimizer for machine learning that would minimize a decomposed function cardinality (DFC) measure of functions would help to solve practical problems in application areas that are of interest to the Pattern Theory Group especially those problems that require strongly unspecified multiple-valued-input functions with a large number of variables. For many functions, the complexity minimization of EXORCISM-MV-2 is better than that of Espresso. For small functions, they are worse than those of the Curtis-like Decomposer. However, EXORCISM is much faster, can run on problems with more variables, and significant DFC improvements have also been found. We analyze the cases when EXORCISM is worse than Espresso and propose new improvements for strongly unspecified functions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46500. 	[Title] Learning Complex Boolean Functions: Algorithms and Applications [Abstract] The most commonly used neural network models are not well suited to direct digital implementations because each node needs to perform a large number of operations between floating point values. Fortunately, the ability to learn from examples and to generalize is not restricted to networks of this type. Indeed, networks where each node implements a simple Boolean function (Boolean networks) can be designed in such a way as to exhibit similar properties. Two algorithms that generate Boolean networks from examples are presented. The results show that these algorithms generalize very well in a class of problems that accept compact Boolean network descriptions. The techniques described are general and can be applied to tasks that are not known to have that characteristic. Two examples of applications are presented: image reconstruction and hand-written character recognition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #57764. 	[Title] Inductive Learning by Selection of Minimal Complexity Representations [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46501. 	[Title] Decision Graphs An Extension of Decision Trees [Abstract] Technical Report No: 92/173 (C) Jonathan Oliver 1992 Shortened appeared in AI and Statistics 1993[14] Abstract: In this paper, we examine Decision Graphs, a generalization of decision trees. We present an inference scheme to construct decision graphs using the Minimum Message Length Principle. Empirical tests demonstrate that this scheme compares favourably with other decision tree inference schemes. This work provides a metric for comparing the relative merit of the decision tree and decision graph formalisms for a particular domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #334153. 	[Title] Machine Learning by Function Decomposition [Abstract] We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of digital circuits. To cope with high time complexity of finding an optimal decomposition, we propose a suboptimal heuristic algorithm. The method, implemented in program HINT (HIerarchy Induction Tool), is experimentally evaluated using a set of artificial and real-world learning problems. It is shown that the method performs well both in terms of classification accuracy and discovery of meaningful concept hierarchies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36140. 	[Title] Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs [Abstract] Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k &gt; 2 values (i.e., k "classes"). The definition is acquired by studying large collections of training examples of the form hx i ; f(x i )i. Existing approaches to this problem include (a) direct application of multiclass algorithms such as the decision-tree algorithms ID3 and CART, (b) application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and (c) application of binary concept learning algorithms with distributed output codes such as those employed by Sejnowski and Rosenberg in the NETtalk system. This paper compares these three approaches to a new technique in which BCH error-correcting codes are employed as a distributed output representation. We show that these output representations improve the performance of ID3 on the NETtalk task and of backpropagation on an isolated-letter speech-recognition task. These results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #216878. 	[Title] Pattern Theoretic Feature Extraction and Constructive Induction [Abstract] This paper offers a perspective on features and pattern finding in general. This perspective is based on a robust complexity measure called Decomposed Function Car-dinality. A function decomposition algorithm for minimizing this complexity measure and finding the associated features is outlined. Results from experiments with this algorithm are also summarized. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12337. 	[Title] A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction [Abstract] Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. dolce consists of a standard recurrent neural net trained by gradient descent and an adaptive clustering technique that quantizes the state space. dolce is based on the assumption that a finite set of discrete internal states is required for the task, and that the actual network state belongs to this set but has been corrupted by noise due to inaccuracy in the weights. dolce learns to recover the discrete state with maximum a posteriori probability from the noisy state. Simulations show that dolce leads to a significant improvement in generalization performance over earlier neural net approaches to FSM induction. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1117618. 	[Title] A dataset decomposition approach to data mining and machine discovery [Abstract] We present a novel data mining approach based on decomposition. In order to analyze a given dataset, the method decomposes it to a hierarchy of smaller and less complex datasets that can be analyzed independently. The method is experimentally evaluated on a real-world housing loans allocation dataset, showing that the decomposition can (1) discover meaningful intermediate concepts, (2) decompose a relatively complex dataset to datasets that are easy to analyze and comprehend, and (3) derive a classifier of high classification accuracy. We also show that human interaction has a positive effect on both the comprehensibility and classification accuracy. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1246. 	[Title] DESIGN AND ANALYSIS OF EFFICIENT REINFORCEMENT LEARNING ALGORITHMS [Abstract]  Please summarise all the given information.

Paper #711527 has the title `APPLICATION OF ESOP MINIMIZATION IN MACHINE LEARNING AND KNOWLEDGE DISCOVERY  
` and the abstract `This paper presents a new application of an Exclusive-Sum-Of-Products (ESOP) minimizer EXORCISM-MV-2: to Machine Learning, and particularly, in Pattern Theory. An analysis of various logic synthesis programs has been conducted at Wright Laboratory for machine learning applications. Creating a robust and efficient Boolean minimizer for machine learning that would minimize a decomposed function cardinality (DFC) measure of functions would help to solve practical problems in application areas that are of interest to the Pattern Theory Group especially those problems that require strongly unspecified multiple-valued-input functions with a large number of variables. For many functions, the complexity minimization of EXORCISM-MV-2 is better than that of Espresso. For small functions, they are worse than those of the Curtis-like Decomposer. However, EXORCISM is much faster, can run on problems with more variables, and significant DFC improvements have also been found. We analyze the cases when EXORCISM is worse than Espresso and propose new improvements for strongly unspecified functions. 
`.
Question: Which category should Paper #711527 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #711527 has the title `APPLICATION OF ESOP MINIMIZATION IN MACHINE LEARNING AND KNOWLEDGE DISCOVERY  
` and the abstract `This paper presents a new application of an Exclusive-Sum-Of-Products (ESOP) minimizer EXORCISM-MV-2: to Machine Learning, and particularly, in Pattern Theory. An analysis of various logic synthesis programs has been conducted at Wright Laboratory for machine learning applications. Creating a robust and efficient Boolean minimizer for machine learning that would minimize a decomposed function cardinality (DFC) measure of functions would help to solve practical problems in application areas that are of interest to the Pattern Theory Group especially those problems that require strongly unspecified multiple-valued-input functions with a large number of variables. For many functions, the complexity minimization of EXORCISM-MV-2 is better than that of Espresso. For small functions, they are worse than those of the Curtis-like Decomposer. However, EXORCISM is much faster, can run on problems with more variables, and significant DFC improvements have also been found. We analyze the cases when EXORCISM is worse than Espresso and propose new improvements for strongly unspecified functions. 
`.
Question: Which category should Paper #711527 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