Aiko M. Hormann

Bio: Aiko M. Hormann is an academic researcher from System Development Corporation. The author has contributed to research in topics: Mathematical proof & Heuristics. The author has an hindex of 3, co-authored 4 publications receiving 3736 citations.

Programs for machine learning Part I

Abstract: This paper reports on a proposed schema and gives some detailed specifications for constructing a learning system by means of programming a computer. We have tried to separate learning processes and problem-solving techniques from specific problem content in order to achieve generality, i.e., in order to achieve a system capable of performing in a wide variety of learning and problem-solving situations. Behavior of the system is determined by both a direct and an indirect means. The former involves detailed, explicit specification of responses or response patterns in the form of built-in programs. The indirect means is by programs representing three mechanisms: a “community unit” (a program-providing mechanism), a planning mechanism, and an induction mechanism. These mechanisms have in common the following features: (1) a directly given repertory of response patterns; (2) general and less explicitly specified decision making rules and hierarchically distributed authority for decision making; (3) an ability to delegate some control over the system's behavior to the environment; and (4) a self-modifying ability which allows the decision-making rules and the repertory of response patterns to adapt and grow. In Part I of this paper, the community unit is described and an illustration of its operation is given. It is presented in a schematized framework as a team of routines connected by first and second-order feedback loops. The function of the community unit is to provide higher-level programs (its environment or customers) with programs capable of performing requested tasks, or to perform a customer-stipulated task by executing a program. If the community unit does not have a ready-made program in stock to fill a particular request, internal programming will be performed, i.e., the community unit will have to construct a program, and debug it, before outputting or executing it. The primary purpose of internal programming is to assist higher-level programs in performing tasks for which detailed preplanning by an external programmer is either impossible or impractical. Some heuristics are suggested for enabling the community unit to search for a usable sequence of operations more efficiently than if it were to search simply by exhaustive or random trial and error. These heuristics are of a step-by-step nature. For complex problems, however, such step-by-step heuristics alone will fail unless there is also a mechanism for analyzing problem structure and placing guideposts on the road to the goal. A planning mechanism capable of doing this is proposed in Part II. Under the control of a higher-level program which specifies the level of detail required in a plan being developed, this planning mechanism is to break up problems into a hierarchy of subproblems each by itself presumably easier to solve than the original problem. To manage classes of problems and to make efficient use of past experience, an induction mechanism is proposed in Part II. An illustration is given of the induction mechanism solving a specific sequence of tasks. The system is currently being programmed and tested in IPL-V on the Philco 2000 computer. The current stage of the programming effort is reported in an epilogue to Part II.

A man-machine synergistic approach to planning and creative problem solving: Part II†

Abstract: This paper describes a proposed system, Gaku, as a step toward man-machine synergism. Characteristics of planning processes are described in terms of the levels of planning (conceptual, definitional, developmental, and operational) and the stages of problem solving (goal setting, alternative generation, consequence estimation, and evaluation and alternative selection). Structural attributes extracted from these characteristics constitute the basic framework and guiding mechanism for man's interaction with Gaku. An example of man-machine interaction is presented, suggesting desirable capabilities of Gaku. Features of Gaku are then described in terms of both built-in caPabilities that are relatively problem independent, and man-machine actions for dynamic extension of these capabilities that are problem dependent and user oriented. The latter can be seen to make the system increasingly useful and powerful as a “co-evolving” man-machine team.

How a computer system can learn

Abstract: The process by which a computer can learn is demonstrated by asking it to solve increasingly more difficult versions of the Tower of Hanoi puzzle Ultimately, the system may learn how to generalize in a particular problem domain Present uses of computers, valuable as they may be, are far from the ultimate in what might be accomplished One of the reasons is that the solution of even well-defined problems, for which goals and rules are precisely known, can be extremely difficult to program (eg, chess-playing programs) But intellectual capacities of machines might be extended by means of an adaptive system to handle increasingly complex and varied tasks

Computer languages for heuristic processes

Abstract: The applicability of computers to many problem-solving situations—chess playing, mathematical proofs, music composition, etc.—has been demonstrated. We wish to explore further heuristic processes suggested by human problem-solving activity. One step toward this goal has been the design of a system named ROVER. ROVER refers both to the simulated computer and the information processing language it uses.

A Short Introduction to Boosting

Abstract: Boosting is a general method for improving the accuracy of any given learning algorithm. This short overview paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting as well as boosting’s relationship to support-vector machines. Some examples of recent applications of boosting are also described.

Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning

Abstract: In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced data sets and their broad application domains in data mining, and then summarizes the evaluation metrics and the existing methods to evaluate and solve the imbalance problem. Synthetic minority over-sampling technique (SMOTE) is one of the over-sampling methods addressing this problem. Based on SMOTE method, this paper presents two new minority over-sampling methods, borderline-SMOTE1 and borderline-SMOTE2, in which only the minority examples near the borderline are over-sampled. For the minority class, experiments show that our approaches achieve better TP rate and F-value than SMOTE and random over-sampling methods.

Foundations of Machine Learning

Abstract: This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book. The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

Abstract: This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.