Unifying Instance-Based and Rule
... Editor: Raymond J. Mooney Abstract. Several well-developed approaches to inductive learning now exist, but each has specific limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem by combining multiple methods in one algorithm. This article describes a unifica ...
... Editor: Raymond J. Mooney Abstract. Several well-developed approaches to inductive learning now exist, but each has specific limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem by combining multiple methods in one algorithm. This article describes a unifica ...
Accelerating the speed and accessibility of artificial intelligence
... sufficient quantities of both. Enabling technologies for the industry vision of artificial intelligence go hand-in-hand with this approach, as they rely on machines sifting through massive data volumes to understand and extract new features, deriving a data-driven accurate model of the decision maki ...
... sufficient quantities of both. Enabling technologies for the industry vision of artificial intelligence go hand-in-hand with this approach, as they rely on machines sifting through massive data volumes to understand and extract new features, deriving a data-driven accurate model of the decision maki ...
Stages of Cognitive Development in Uncertain-Logic
... abstract logical representations of the observational world. --Formal: Abstract deductive reasoning, the process of forming, then testing hypotheses, and systematically reevaluating and refining solutions, develops at this stage, as does the ability to reason about purely abstract concepts without r ...
... abstract logical representations of the observational world. --Formal: Abstract deductive reasoning, the process of forming, then testing hypotheses, and systematically reevaluating and refining solutions, develops at this stage, as does the ability to reason about purely abstract concepts without r ...
Pareto-Based Multiobjective Machine Learning: An
... and generating negatively correlated ensemble members [8]. Unlike neural networks and fuzzy systems for regression and classification, where complexity control is not a must, some learning models, like support vector machines [9], sparse coding [10], or learning tasks, such as receiver operating cha ...
... and generating negatively correlated ensemble members [8]. Unlike neural networks and fuzzy systems for regression and classification, where complexity control is not a must, some learning models, like support vector machines [9], sparse coding [10], or learning tasks, such as receiver operating cha ...
A Personalized Calendar Assistant - SRI Artificial Intelligence Center
... letting the system suggest more than one (but not too many) candidate schedules, we increase the probability that the user will find an acceptable suggestion without having to consider all alternatives. However, when a user selects a suggested schedule (or overrides all suggestions), the only feedba ...
... letting the system suggest more than one (but not too many) candidate schedules, we increase the probability that the user will find an acceptable suggestion without having to consider all alternatives. However, when a user selects a suggested schedule (or overrides all suggestions), the only feedba ...
neural network for multitask learning applied in electronics games
... Minas Gerais, Departamento de Computação, Brazil. ...
... Minas Gerais, Departamento de Computação, Brazil. ...
Characteristics Analysis for Small Data Set Learning and
... good idea. The virtual data concept is used in many small data set learning methods. It was first proposed by Niyogi et al. [10] in the study of human face recognition. In their study, virtual face recognition data from any other direction can be generated using given view data through mathematical ...
... good idea. The virtual data concept is used in many small data set learning methods. It was first proposed by Niyogi et al. [10] in the study of human face recognition. In their study, virtual face recognition data from any other direction can be generated using given view data through mathematical ...
This Is a Publication of The American Association for Artificial
... In the area of semantic interpretation, there have been a number of interesting uses of corpus-based techniques. Some researchers have used empirical techniques to address a difficult subtask of semantic interpretation, that of developing accurate rules to select the proper meaning, or sense, of a s ...
... In the area of semantic interpretation, there have been a number of interesting uses of corpus-based techniques. Some researchers have used empirical techniques to address a difficult subtask of semantic interpretation, that of developing accurate rules to select the proper meaning, or sense, of a s ...
PowerPoint 簡報
... Approximate Reasoning for Fuzzy sets The most used inference rule is (A1(A2B))B. In the classic logic, either A1=A2 or A1A2. Therefore, with the match and fire property, either B is concluded or B is not concluded. But, with the use of fuzzy sets, either A1 or A2 is a fuzzy set or both. Then wha ...
... Approximate Reasoning for Fuzzy sets The most used inference rule is (A1(A2B))B. In the classic logic, either A1=A2 or A1A2. Therefore, with the match and fire property, either B is concluded or B is not concluded. But, with the use of fuzzy sets, either A1 or A2 is a fuzzy set or both. Then wha ...
Learning bayesian network structure using lp relaxations Please share
... & Koivisto [2009]) or approximate methods based on local or stochastic search. Without additional constraints, exact methods are limited to relatively small problems (around 30 nodes) as both computation and memory requirements scale exponentially with the number of nodes in the graph. Local search ...
... & Koivisto [2009]) or approximate methods based on local or stochastic search. Without additional constraints, exact methods are limited to relatively small problems (around 30 nodes) as both computation and memory requirements scale exponentially with the number of nodes in the graph. Local search ...
Ontology construction for information classification
... Maedche and Staab (2001) mentioned that ontology learning can be divided into four parts: extract, prune, refine, import or reuse. We will, for now, direct our attention to the extraction methods upon which we will base our research findings. There are four categories in the construction methods of ...
... Maedche and Staab (2001) mentioned that ontology learning can be divided into four parts: extract, prune, refine, import or reuse. We will, for now, direct our attention to the extraction methods upon which we will base our research findings. There are four categories in the construction methods of ...
Understanding Math Objects
... misunderstandings because readers apply AM when they see the noun cognition. They wonder where the acquired cognitive objects are possessed and stored, since there is no individual physical persisting agent involved. If one applies PM instead, in line with Sfard’s theory, then it makes much more sen ...
... misunderstandings because readers apply AM when they see the noun cognition. They wonder where the acquired cognitive objects are possessed and stored, since there is no individual physical persisting agent involved. If one applies PM instead, in line with Sfard’s theory, then it makes much more sen ...
also available as Word 2000 ()
... identifying and implementing the most general and foundational components first, leaving high-level cognition such as abstract thought, language, and formal logic for later development (more on that later). We also focus more on selective, unsupervised, dynamic, incremental, interactive learning; on ...
... identifying and implementing the most general and foundational components first, leaving high-level cognition such as abstract thought, language, and formal logic for later development (more on that later). We also focus more on selective, unsupervised, dynamic, incremental, interactive learning; on ...
S - GdR-IQFA
... (2) Action is induced by screening clips for specific features. detection of feature triggers motor action. (3) Learning is realized by changing the network: altering hopping probabilities altering network geometry (addition of clips) ...
... (2) Action is induced by screening clips for specific features. detection of feature triggers motor action. (3) Learning is realized by changing the network: altering hopping probabilities altering network geometry (addition of clips) ...
Using Evidence-Centered Design for Developing Valid
... evidence that is needed to support the above-mentioned claims. Evidence models describe what is to be scored, how to score it, and how to combine scores into claims. It consists of two parts: evidence rules, which include rubrics or scoring models and statistical models, which range from a simple nu ...
... evidence that is needed to support the above-mentioned claims. Evidence models describe what is to be scored, how to score it, and how to combine scores into claims. It consists of two parts: evidence rules, which include rubrics or scoring models and statistical models, which range from a simple nu ...
Ontology learning from text based on multi
... fact that usually many different specialists have to co-operate for this task, while they must agree on certain design choices5 . In addition, it is hard to organise a group of experts for each possible domain. An approach that could dramatically reduce the tedious work and the huge cost of building ...
... fact that usually many different specialists have to co-operate for this task, while they must agree on certain design choices5 . In addition, it is hard to organise a group of experts for each possible domain. An approach that could dramatically reduce the tedious work and the huge cost of building ...
Agent Shell for the Development of Tutoring Systems for Expert
... rule expresses how and under what conditions a generic problem can be reduced to simpler generic problems. A solution synthesis rule expresses how and under what conditions the solutions of generic subproblems can be combined into the solution of a generic problem. The conditions are complex first-o ...
... rule expresses how and under what conditions a generic problem can be reduced to simpler generic problems. A solution synthesis rule expresses how and under what conditions the solutions of generic subproblems can be combined into the solution of a generic problem. The conditions are complex first-o ...
Evaluation of General-Purpose Artificial Intelligence
... we refer to whatever thing is being tested, whether that includes a body or not. Intelligent systems interact with task-environments, which are tuples of a task and an environment. An environment contains objects that a system-under-test can interact with—which may form larger complex systems such a ...
... we refer to whatever thing is being tested, whether that includes a body or not. Intelligent systems interact with task-environments, which are tuples of a task and an environment. An environment contains objects that a system-under-test can interact with—which may form larger complex systems such a ...
Machine Learning CSCI 5622
... goal achievement (accomplishing tasks that Greg doesn’t feel like doing), given the available information ...
... goal achievement (accomplishing tasks that Greg doesn’t feel like doing), given the available information ...
Learning Belief Networks in the Presence of Missing - CS
... sponds to a random variable. This graph represents a set of conditional independence properties of the represented distribution. This component captures the structure of the probability distribution, and is exploited for efficient inference and decision making. Thus, while belief networks can repres ...
... sponds to a random variable. This graph represents a set of conditional independence properties of the represented distribution. This component captures the structure of the probability distribution, and is exploited for efficient inference and decision making. Thus, while belief networks can repres ...
One-class to multi-class model update using the class
... AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume, while the papers from STAIRS are published in a separate volume. ECAI 2016 also featured a special topic on Artificial Intelligence for Human Values, with a dedicated track and a public event in the Peace Palace in T ...
... AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume, while the papers from STAIRS are published in a separate volume. ECAI 2016 also featured a special topic on Artificial Intelligence for Human Values, with a dedicated track and a public event in the Peace Palace in T ...
Analogy-based Reasoning With Memory Networks - CEUR
... function l(el , er ) = zTl M zr , where zl and zr are the concatenated word embeddings xs , xvl , xo and xs , xvr , xo , respectively, and parameter matrix M ∈ R3d×3d . We denote this model as Bai2009. We also test three neural network architecture that were proposed in different contexts. The model ...
... function l(el , er ) = zTl M zr , where zl and zr are the concatenated word embeddings xs , xvl , xo and xs , xvr , xo , respectively, and parameter matrix M ∈ R3d×3d . We denote this model as Bai2009. We also test three neural network architecture that were proposed in different contexts. The model ...
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia
... dopamine-like reinforcement learning mechanisms in the rat’s basal ganglia (Houk et al., 1995). In such models, an Actor network learns to select actions in order to maximize the weighted sum of future rewards, as computed on line by another network, a Critic. The Critic predicts this sum by compari ...
... dopamine-like reinforcement learning mechanisms in the rat’s basal ganglia (Houk et al., 1995). In such models, an Actor network learns to select actions in order to maximize the weighted sum of future rewards, as computed on line by another network, a Critic. The Critic predicts this sum by compari ...
5 Artificial Intelligence perspectives
... been working since the 1990s. Amazon offers Amazon Machine Learning Service as part of the company’s suite of Amazon Web Services (AWS), for any business to use its technology, and it is designed for developers with no experience in machine learning. Baidu, the Chinese internet giant, has opened a r ...
... been working since the 1990s. Amazon offers Amazon Machine Learning Service as part of the company’s suite of Amazon Web Services (AWS), for any business to use its technology, and it is designed for developers with no experience in machine learning. Baidu, the Chinese internet giant, has opened a r ...
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Paper []
... We begin testing our method in the simplest environment (Figure 2) with a distinguishing feature (the notch) small enough to be obscured by image variability. Lassie is a RWI Magellan robot. It perceives its environment using a laser range-finder: each sensory image o is a point in R180 , representi ...
... We begin testing our method in the simplest environment (Figure 2) with a distinguishing feature (the notch) small enough to be obscured by image variability. Lassie is a RWI Magellan robot. It perceives its environment using a laser range-finder: each sensory image o is a point in R180 , representi ...