Contributions to Deep Learning Models - RiuNet
... Error rate (\%) on the MNIST test set for both clean and noisy images using different regularization schemes and varying the number of labeled samples used in the fine-tuning. . . . . . . . . . . . . . . . . . Effect of applying MSR to different layers of the network. Error rate (\%) on the MNIST te ...
... Error rate (\%) on the MNIST test set for both clean and noisy images using different regularization schemes and varying the number of labeled samples used in the fine-tuning. . . . . . . . . . . . . . . . . . Effect of applying MSR to different layers of the network. Error rate (\%) on the MNIST te ...
Flexible Attention-based Cognitive Architecture for
... engineering. Minsky has argued in his Society of Mind that intelligent behaviours can emerge from the interaction of many simple processes, even though each process may lack ‘intelligence’ in isolation. In addition, Anderson argued that an emergent system produces more complex behaviours and propert ...
... engineering. Minsky has argued in his Society of Mind that intelligent behaviours can emerge from the interaction of many simple processes, even though each process may lack ‘intelligence’ in isolation. In addition, Anderson argued that an emergent system produces more complex behaviours and propert ...
Towards Smart User Models for Open Environments
... Figure 1. The incidence of Agent Technology in several research areas………………….38 Figure 2. General Process of Kansei Engineering………………………………………...49 Figure 3. The current scenario in Recommender Systems……………………………….. 72 Figure 4. The Next-Generation of Recommender Systems............................. ...
... Figure 1. The incidence of Agent Technology in several research areas………………….38 Figure 2. General Process of Kansei Engineering………………………………………...49 Figure 3. The current scenario in Recommender Systems……………………………….. 72 Figure 4. The Next-Generation of Recommender Systems............................. ...
uma modelagem dos processos cognitivo, emocional e motivacional
... FCM system considering the interaction among the concepts. The results have shown quite coherence in relation to expected outcomes based on common sense experiences and in experts’ beliefs. The quality of them allows concluding that FCM tool is able to model psychological processes making possible i ...
... FCM system considering the interaction among the concepts. The results have shown quite coherence in relation to expected outcomes based on common sense experiences and in experts’ beliefs. The quality of them allows concluding that FCM tool is able to model psychological processes making possible i ...
An Investigation of the Cost and Accuracy Tradeoffs of Supplanting... in Query Processing in the Presence of Incompleteness in Autonomous...
... missing values—which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilistic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it st ...
... missing values—which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilistic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it st ...
Viewpoints
... are symbols describing a discrete state of some part of the world. For example, HEIGHT(TALL) describes an interactor (human or AI) standing at full height, with related predicates HEIGHT(SHORT) or HEIGHT(MEDIUM). The Viewpoints AI system’s reasoning module (Decision Making from Figure 1) uses this s ...
... are symbols describing a discrete state of some part of the world. For example, HEIGHT(TALL) describes an interactor (human or AI) standing at full height, with related predicates HEIGHT(SHORT) or HEIGHT(MEDIUM). The Viewpoints AI system’s reasoning module (Decision Making from Figure 1) uses this s ...
Viewpoints AI
... are symbols describing a discrete state of some part of the world. For example, HEIGHT(TALL) describes an interactor (human or AI) standing at full height, with related predicates HEIGHT(SHORT) or HEIGHT(MEDIUM). The Viewpoints AI system’s reasoning module (Decision Making from Figure 1) uses this s ...
... are symbols describing a discrete state of some part of the world. For example, HEIGHT(TALL) describes an interactor (human or AI) standing at full height, with related predicates HEIGHT(SHORT) or HEIGHT(MEDIUM). The Viewpoints AI system’s reasoning module (Decision Making from Figure 1) uses this s ...
Literature Review on Feature Selection Methods for High
... as possible combinations of feature subsets using any one of the searching strategies. Then, the feature subsets are evaluated using any one of the statistical measures or the supervised learning algorithms to observe the significance of each subset and the most significant subset is selected as the ...
... as possible combinations of feature subsets using any one of the searching strategies. Then, the feature subsets are evaluated using any one of the statistical measures or the supervised learning algorithms to observe the significance of each subset and the most significant subset is selected as the ...
Philosophical Aspects in Pattern Recognition Research
... psychological considerations or biological concepts, includes also many philosophical assertions ([66, 114] spring to mind). The same Dartmouth proposal seems to hold that the technological enterprise supposes, in a way, a cognitive demand1 theorizing that: “every aspect of learning or any feature o ...
... psychological considerations or biological concepts, includes also many philosophical assertions ([66, 114] spring to mind). The same Dartmouth proposal seems to hold that the technological enterprise supposes, in a way, a cognitive demand1 theorizing that: “every aspect of learning or any feature o ...
Soft computing is an association of computing
... • Is a subfield of artificial intelligence (computational intelligence) that involves continuous optimization and combinatorial optimization problems. • Evolutionary computing uses iterative progress, such as growth or development in a population. This population is then selected in a guided random ...
... • Is a subfield of artificial intelligence (computational intelligence) that involves continuous optimization and combinatorial optimization problems. • Evolutionary computing uses iterative progress, such as growth or development in a population. This population is then selected in a guided random ...
Toward Narrative Schema-Based Goal Recognition Models for Interactive Narrative Environments
... information about narrative structures that occur during interactive narrative experiences. The concept of narrative schemas is drawn from related work in the natural language processing (NLP) community that focuses on automatically capturing salient information about narrative sequences that recur ...
... information about narrative structures that occur during interactive narrative experiences. The concept of narrative schemas is drawn from related work in the natural language processing (NLP) community that focuses on automatically capturing salient information about narrative sequences that recur ...
Toward Narrative Schema-Based Goal Recognition Models for
... information about narrative structures that occur during interactive narrative experiences. The concept of narrative schemas is drawn from related work in the natural language processing (NLP) community that focuses on automatically capturing salient information about narrative sequences that recur ...
... information about narrative structures that occur during interactive narrative experiences. The concept of narrative schemas is drawn from related work in the natural language processing (NLP) community that focuses on automatically capturing salient information about narrative sequences that recur ...
Offline Arabic Character Recognition using Genetic Approach
... every word in the lexicon, compared against analytical models that need only be trained for every character, their application is limited to those with small and constant lexicons, such as reading the courtesy amount on bank checks (Farah et al., 2006; Souici and Sellami, 2006). ...
... every word in the lexicon, compared against analytical models that need only be trained for every character, their application is limited to those with small and constant lexicons, such as reading the courtesy amount on bank checks (Farah et al., 2006; Souici and Sellami, 2006). ...
A Review of Machine Learning Algorithms for Text
... the efficiency and accuracy of classifiers. There are mainly two types of feature selection methods in machine learning; wrappers and filters. Wrappers use ...
... the efficiency and accuracy of classifiers. There are mainly two types of feature selection methods in machine learning; wrappers and filters. Wrappers use ...
opinion mining and sentiment classification: a
... The political orientation of the websites can be done by classifying the concatenation of all the documents found on that particular site as in [9]. Analyzing sentiment and opinions in political oriented text, generally focuses on the attitude expressed via texts which are not targeted at a specific ...
... The political orientation of the websites can be done by classifying the concatenation of all the documents found on that particular site as in [9]. Analyzing sentiment and opinions in political oriented text, generally focuses on the attitude expressed via texts which are not targeted at a specific ...
Ensemble Learning Techniques for Structured
... Introduction ..................................................................................................................................... 1 1. Ensemble methods overview ................................................................................................... 1 ...
... Introduction ..................................................................................................................................... 1 1. Ensemble methods overview ................................................................................................... 1 ...
