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Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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TL;DR: This paper proposes a method that directly learns decision forests via fully-corrective regularized greedy search using the underlying forest structure and achieves higher accuracy and smaller models than gradient boosting on many of the datasets it has tested on.
Abstract: We consider the problem of learning a forest of nonlinear decision rules with general loss functions. The standard methods employ boosted decision trees such as Adaboost for exponential loss and Friedman's gradient boosting for general loss. In contrast to these traditional boosting algorithms that treat a tree learner as a black box, the method we propose directly learns decision forests via fully-corrective regularized greedy search using the underlying forest structure. Our method achieves higher accuracy and smaller models than gradient boosting (and Adaboost with exponential loss) on many datasets.
113 citations
Proceedings Article•
01 Aug 1996TL;DR: It is argued that it is often appropriate to search among equivalence classes of network structures as opposed to the more common approach of searching among individual Bayesian-network structures, and a convenient graphical representation for an equivalence class of structures is described and a set of operators that can be applied to that representation by a search algorithm to move among equivalENCE classes are introduced.
Abstract: Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a scoring function, it is appropriate for the heuristic search algorithm to search over equivalence classes of Bayesian networks as opposed to individual structures. We present the general formulation of a search space for which the states of the search correspond to equivalence classes of structures. Using this space, any one of a number of heuristic search algorithms can easily be applied. We compare greedy search performance in the proposed search space to greedy search performance in a search space for which the states correspond to individual Bayesian network structures.
113 citations
Proceedings Article•
01 Dec 1995TL;DR: For any fixed p, 1 < p < M, it is shown that the greedy algorithm performs within a con- stant factor of the ofline optimal with respect to the L, norm, which grows linearly with p, which is best possible, but does not depend on the number of servers and jobs.
Abstract: In the load balancing problem, there is a set of servers, and jobs arrive sequentially. Each job can be run on some subset of the servers, and must be assigned to one of them in an online fashion. Tradi- tionally, the assignment of jobs to servers is measured by the L, norm; in other words, an assignment of jobs to servers is quantified by the maximum load as- signed to any server. In this measure the performance of the greedy load balancing algorithm may be a loga- rithmic factor higher than the ofline optimal (3). In many applications, the L, norm is not a suitable way to measure how well the jobs are balanced. If each job sees a delay that is proportional to the number of jobs on its server, then the average delay among all jobs is proportional to the sum of the squares of the numbers of jobs assigned to the servers. Minimizing the average delay is equivalent to minimizing the Eu- clidean (or Lz) norm. For any fixed p, 1 < p < M, we show that the greedy algorithm performs within a con- stant factor of the ofline optimal with respect to the L, norm. The constant grows linearly with p, which is best possible, but does not depend on the number of servers and jobs.
113 citations
TL;DR: This work proposes a hybrid GA that takes as input the current replica distribution and computes a new one using knowledge about the network attributes and the changes occurred, and evaluates these algorithms with respect to the storage capacity constraint of each site as well as variations in the popularity of objects.
113 citations
TL;DR: This paper presents a simple and efficient automatic mesh segmentation algorithm that solely exploits the shape concavity information and employs a score-based greedy algorithm to select the best cuts.
Abstract: This paper presents a simple and efficient automatic mesh segmentation algorithm that solely exploits the shape concavity information. The method locates concave creases and seams using a set of concavity-sensitive scalar fields. These fields are computed by solving a Laplacian system with a novel concavity-sensitive weighting scheme. Isolines sampled from the concavity-aware fields naturally gather at concave seams, serving as good cutting boundary candidates. In addition, the fields provide sufficient information allowing efficient evaluation of the candidate cuts. We perform a summarization of all field gradient magnitudes to define a score for each isoline and employ a score-based greedy algorithm to select the best cuts. Extensive experiments and quantitative analysis have shown that the quality of our segmentations are better than or comparable with existing state-of-the-art more complex approaches.
113 citations