Machine Learning

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Is Combining Classifiers with Stacking Better than Selecting the Best One?
Machine Learning - Tập 54 Số 3 - Trang 255-273 - 2004
Sašo Džeroski, Bernard Ženko
Matrix representations, linear transformations, and kernels for disambiguation in natural language
Machine Learning - Tập 74 Số 2 - Trang 133-158 - 2009
Pahikkala, Tapio, Pyysalo, Sampo, Boberg, Jorma, Järvinen, Jouni, Salakoski, Tapio
In the application of machine learning methods with natural language inputs, the words and their positions in the input text are some of the most important features. In this article, we introduce a framework based on a word-position matrix representation of text, linear feature transformations of the word-position matrices, and kernel functions constructed from the transformations. We consider two...... hiện toàn bộ
Những Thuật Toán Giải Quyết Ràng Buộc Để Nâng Cao Tốc Độ Thử Thách Theta-Subsumption Dịch bởi AI
Machine Learning - Tập 55 - Trang 137-174 - 2004
Jérôme Maloberti, Michèle Sebag
Học tập quan hệ và Lập trình Logic Quy nạp (ILP) thường sử dụng thử nghiệm θ-subsumption được định nghĩa bởi Plotkin. Dựa trên việc tái cấu trúc θ-subsumption thành một bài toán thỏa mãn ràng buộc nhị phân, bài báo này mô tả một thuật toán θ-subsumption mới có tên là Django,1 kết hợp giữa các quy trình CSP nổi tiếng và các cấu trúc dữ liệu đặc thù cho θ-subsumption. Django được xác nhận bằng cách ...... hiện toàn bộ
#θ-subsumption #Lập trình Logic Quy nạp #Giải quyết Ràng buộc #Phức tạp Ngẫu nhiên #Django.
Supporting Start-to-Finish Development of Knowledge Bases
Machine Learning - Tập 4 - Trang 259-283 - 1989
Ray Bareiss, Bruce W. Porter, Kenneth S. Murray
Developing knowledge bases using knowledge-acquisition tools is difficult because each stage of development requires performing a distinct knowledge-acquisition task. This paper describes these different tasks and surveys current tools that perform them. It also addresses two issues confronting tools for start-to-finish development of knowledge bases. The first issue is how to support multiple sta...... hiện toàn bộ
Learning boolean functions in an infinite attribute space
Machine Learning - Tập 9 - Trang 373-386 - 1992
Avrim Blum
This paper presents a theoretical model for learning Boolean functions in domains having a large, potentially infinite number of attributes. The model allows an algorithm to employ a rich vocabulary to describe the objects it encounters in the world without necessarily incurring time and space penalties so long as eachindividual object is relatively simple. We show that many of the basic Boolean f...... hiện toàn bộ
Towards harnessing feature embedding for robust learning with noisy labels
Machine Learning - - 2022
Chuang Zhang, Li Shen, Jian Yang, Chen Gong
Inverse reinforcement learning in contextual MDPs
Machine Learning - Tập 110 - Trang 2295-2334 - 2021
Stav Belogolovsky, Philip Korsunsky, Shie Mannor, Chen Tessler, Tom Zahavy
We consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the agent observes demonstrations from an optimal policy. The goal is to learn the re...... hiện toàn bộ
Boosting Methods for Regression
Machine Learning - - 2002
Nigel Duffy, David Helmbold
In this paper we examine ensemble methods for regression that leverage or “boost” base regressors by iteratively calling them on modified samples. The most successful leveraging algorithm for classification is AdaBoost, an algorithm that requires only modest assumptions on the base learning method for its strong theoretical guarantees. We present several gradient descent leveraging algorithms for ...... hiện toàn bộ
Sequence labeling with multiple annotators
Machine Learning - Tập 95 Số 2 - Trang 165-181 - 2014
Filipe Rodrigues, Francisco C. Pereira, Bernardete Ribeiro
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning - Tập 58 - Trang 5-24 - 2005
Geoffrey I. Webb, Janice R. Boughton, Zhihai Wang
Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and Super-Parent TAN have demonstrated remarkable error performance. However, both techniques obtain this outcome at a considerable computational cost. We present a new approach to weakening the attribute independence assumption by averaging all of a constrained class of classi...... hiện toàn bộ
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