Data/information fusion is an integral component of many existing and emerging applications; e.g., remote sensing, smart cars, Internet of Things (IoT), and Big Data, to name a few. While fusion aims to achieve better results than what any one individual input can provide, often the challenge is to determine the underlying mathematics for aggregation suitable for an application. In this dissertation, I focus on the following three aspects of aggregation: (i) efficient data-driven learning and optimization, (ii) extensions and new aggregation methods, and (iii) feature and decision level fusion for machine learning with applications to signal and image processing. The Choquet integral (ChI), a powerful nonlinear aggregation operator, is a parametric way (with respect to the fuzzy measure (FM)) to generate a wealth of aggregation operators. The FM has 2N variables and N(2N − 1) constraints for N inputs. As a result, learning the ChI parameters from data quickly becomes impractical for most applications. Herein, I propose a scalable learning procedure (which is linear with respect to training sample size) for the ChI that identifies and optimizes only data-supported variables. As such, the computational complexity of the learning algorithm is proportional to the complexity of the solver used. This method also includes an imputation framework to obtain scalar values for data-unsupported (aka missing) variables and a compression algorithm (lossy or losselss) of the learned variables. I also propose a genetic algorithm (GA) to optimize the ChI for non-convex, multi-modal, and/or analytical objective functions. This algorithm introduces two operators that automatically preserve the constraints; therefore there is no need to explicitly enforce the constraints as is required by traditional GA algorithms. In addition, this algorithm provides an efficient representation of the search space with the minimal set of vertices. Furthermore, I study different strategies for extending the fuzzy integral for missing data and I propose a GOAL programming framework to aggregate inputs from heterogeneous sources for the ChI learning. Last, my work in remote sensing involves visual clustering based band group selection and Lp-norm multiple kernel learning based feature level fusion in hyperspectral image processing to enhance pixel level classification.

字彙的誤用，是台灣英語學習者易犯的毛病之一。由於中文與英文字詞並非呈現一對一的語意對應，加上國中英語教師多年來習慣要求學生以記誦中文意思的方式學習英文單字，導致學生常有用錯字的狀況發生，並造成語意上的誤解。有鑑於此，本文作者盼能以語料庫英語教學來改善這些現象，並以see, watch ,look三個意義相近字彙的區辨為例，呈現出完整的教學過程，供英語教師教學或學習者自修參考。 在教學前，教師應根據學生英文程度與教學需求，先行篩選並編輯語料，以利學生學習。在課堂上，藉由這些語料的呈現，讓學生觀察其中的規律性；透過問題的解決，用認知的手段讓學生觀察該字彙出現的語境及其易連結的字串；並以測驗的方式檢測學生的理解程度。此外，本文亦詳細歸納這三個單字的使用時機，並提出實用的區辨方式。 作者盼能藉由語料庫英語教學的實施，增強英語學習者的學習意願。透過類似活動的投入及參與，學習者將不再只是訊息接收者，而能藉由觀察來創造自己的知識、增進對英文的掌握度。 / In Taiwan, many EFL students have difficulty using the proper vocabulary at the right time. Due to the fact that Mandarin and English vocabulary are in a one-to-many semantically-corresponding relationship, and that Taiwanese EFL students are often taught to learn English vocabulary by memorizing its Mandarin equivalent, students often have difficulties choosing proper English equivalents in different contexts. Besides, the arrangement of junior high school English curriculum has made it even more difficult for students to learn vocabulary accurately. Because the improper use of vocabulary often brings about confusion or misunderstanding, a practical method is needed to cope with the concerned problems. Nowadays, Computer-assisted Language Learning (CALL) has been a trend. In this study, the researcher will take “see, watch, and look at” as examples to show a corpora-based teaching procedure. Subjects are 8th graders in junior high school. The scope of the research is confined to the prototypical meanings of these verbs; it is believed that students should possess the basic knowledge about these verbs before they continue to learn other extended meanings. Before the class, the conordance lines are selected and carefully edited by the teacher to meet the needs of the course. In class, consciousness-raising tasks, combined with quizzes and complementary materials, provide students with comprehensive knowledge about the three verbs. After the activities, crucial information of the verbs is clearly exhibited, and useful methods are also presented to help distinguish the verbs. With the “context providers”, namely, the corpora, both teachers and students are provided with authentic and plentiful examples, which are often insufficient for Taiwanese EFL learners. Through the participation of these activities, students become participants and create their own knowledge. It is hoped that with the assistance of data-driven learning (DDL), EFL teachers will then be able to provide their students with not only more reliable information, but more constructive and systematic instruction.