Above are papers related to the following paper: [Paper_ID] Paper #8961. 	[Title] Machine Learning Research: Four Current Directions [Abstract] Machine Learning research has been making great progress is many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods for scaling up supervised learning algorithms, (c) reinforcement learning, and (d) learning complex stochastic models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103610. 	[Title] A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment [Abstract] Classifying by hand complex data coming from psychology experiments can be a long and difficult task, because of the quantity of data to classify and the amount of training it may require. One way to alleviate this problem is to use machine learning techniques. We built a classifier based on decision trees that reproduces the classifying process used by two humans on a sample of data and that learns how to classify unseen data. The automatic classifier proved to be more accurate, more constant and much faster than classification by hand. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3243. 	[Title] A System for Induction of Oblique Decision Trees [Abstract] This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133390. 	[Title] Decision Trees: Equivalence and Propositional Operations [Abstract] For the well-known concept of decision trees as it is used for inductive inference we study the natural concept of equivalence: two decision trees are equivalent if and only if they represent the same hypothesis. We present a simple efficient algorithm to establish whether two decision trees are equivalent or not. The complexity of this algorithm is bounded by the product of the sizes of both decision trees. The hypothesis represented by a decision tree is essentially a boolean function, just like a proposition. Although every boolean function can be represented in this way, we show that disjunctions and conjunctions of decision trees can not efficiently be represented as decision trees, and simply shaped propositions may require exponential size for representation as de cision trees. Please summarise all the given information.

Paper #8961 has the title `Machine Learning Research: Four Current Directions  
` and the abstract `Machine Learning research has been making great progress is many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods for scaling up supervised learning algorithms, (c) reinforcement learning, and (d) learning complex stochastic models. 
`.
Question: Which category should Paper #8961 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #8961 has the title `Machine Learning Research: Four Current Directions  
` and the abstract `Machine Learning research has been making great progress is many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods for scaling up supervised learning algorithms, (c) reinforcement learning, and (d) learning complex stochastic models. 
`.
Question: Which category should Paper #8961 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: Reinforcement_Learning