In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Example Input: We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified average feature acquisition budget. While random forests yield strong generalization performance, they do not explicitly account for feature costs and furthermore require low correlation among trees, which amplifies costs. Our random forest grows trees with low acquisition cost and high strength based on greedy minimax cost-weighted-impurity splits. Theoretically, we establish near-optimal acquisition cost guarantees for our algorithm. Empirically, on a number of benchmark datasets we demonstrate superior accuracy-cost curves against state-of-the-art prediction-time algorithms.
Example Output: Feature-Budgeted Random Forest

Example Input: A famous biologically inspired hierarchical model firstly proposed by Riesenhuber and Poggio has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of positionand scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental results, we introduce the Memory and Association Mechanisms into the above biologically inspired model. The main motivations of the work are (a) to mimic the active memory and association mechanism and add the ’top down’ adjustment to the above biologically inspired hierarchical model and (b) to build up an algorithm which can save the space and keep a good recognition performance. More details of the work could be explained as follows: (1) In objects memorizing process: Our proposed model mimics some characteristics of human’s memory mechanism as follows: (a) In our model, one object is memorized by semantic attributes and special image patches (corresponding to episodic memory). The semantic attributes describe each part of the object with clear physical meaning, for example, if eyes and mouths of faces are ’big’ or ’small’ and so on. One special patch is selected if the value of the corresponding semantic feature is far from average one. The patch should be the most prominent part of the object. (b) In our model, different features (semantic attributes and special patches) of one object are stored in distributed places and the common feature of different objects is saved aggregately, which can learn to classify the difference of similar features of different objects. The similarity thresholds to each object can be learnt when new objects are learnt. (2) In object recognition process: In biological process, the associated recognition including familiarity discrimination and recollective matching. In our proposed model, firstly mimicking familiarity discrimination (’knowing’ though the episode), we compare the special patches of candidates with that of saved objects using above mentioned biologically inspired hierarchical model, where the candidates and saved objects have the same prominent semantic features. Then mimicking recollective matching, the comparison results of special patches are combined with semantic feature comparison. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with much less memory requirement.
Example Output: Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model

Example Input: Many large MDPs can be represented compactly using a dynamic Bayesian network. Although the structure of the value function does not re­ tain the structure of the process, recent work has suggested that value functions in factored MDPs can often be approximated well using a factored value function: a linear combination of restr icted basis functions, each of which refers only to a small subset of variables. An approximate fac­ tored value function for a particular policy can be computed using approximate dynamic pro­ gramming, but this approach (and others) can only produce an approximation relative to a dis­ tance metric which is weighted by the station­ ary distribution of the current policy. This type of weighted projection is ill-suited to policy im­ provement. We present a new approach to value determination, that uses a simple closed-form computation to compute a least-squares decom­ posed approximation to the value function for any weights directly. We then use this value de­ termination algorithm as a subroutine in a pol­ icy iteration process. We show that, under rea­ sonable restrictions, the policies induced by a factored value function can be compactly repre­ sented as a decision list, and can be manipulated efficiently in a policy iteration process. We also present a method for computing error bounds for decomposed value functions using a variable­ elimination algorithm for function optimization. The complexity of all of our algorithms depends on the factorization of the system dynamics and of the approximate value function.
Example Output:
Policy Iteration for Factored MDPs