Search results for "Machine learning"
showing 10 items of 1464 documents
Representation, Recognition and Generation of Actions in the Context of Imitation Learning
2006
The paper deals with the development of a cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. We adopt the paradigm of conceptual spaces, in which static and dynamic entities are employed to efficiently organize perceptual data, to recognize positional relations, to learn movements from human demonstration and to generate complex actions by combining and sequencing simpler ones. The aim is to have a robotic system able to effectively learn by imitation and which has the capabilities of deeply understanding the perceived actions to be imitated. Experimentation has been performed on a robotic system composed of a PUMA 20…
"Table 7" of "Measurement of D*+/- meson production in jets from pp collisions at sqrt(s) = 7 TeV with the ATLAS detector"
2012
Comparison of the reconstructed Z distribution with the reweighted Monte Carlo prediction.
Estimating feature discriminant power in decision tree classifiers
1995
Feature Selection is an important phase in pattern recognition system design. Even though there are well established algorithms that are generally applicable, the requirement of using certain type of criteria for some practical problems makes most of the resulting methods highly inefficient. In this work, a method is proposed to rank a given set of features in the particular case of Decision Tree classifiers, using the same information generated while constructing the tree. The preliminary results obtained with both synthetic and real data confirm that the performance is comparable to that of sequential methods with much less computation.
Towards safe reinforcement-learning in industrial grid-warehousing
2020
Abstract Reinforcement learning has shown to be profoundly successful at learning optimal policies for simulated environments using distributed training with extensive compute capacity. Model-free reinforcement learning uses the notion of trial and error, where the error is a vital part of learning the agent to behave optimally. In mission-critical, real-world environments, there is little tolerance for failure and can cause damaging effects on humans and equipment. In these environments, current state-of-the-art reinforcement learning approaches are not sufficient to learn optimal control policies safely. On the other hand, model-based reinforcement learning tries to encode environment tra…
Learned Sorted Table Search and Static Indexes in Small-Space Data Models
2023
Machine-learning techniques, properly combined with data structures, have resulted in Learned Static Indexes, innovative and powerful tools that speed up Binary Searches with the use of additional space with respect to the table being searched into. Such space is devoted to the machine-learning models. Although in their infancy, these are methodologically and practically important, due to the pervasiveness of Sorted Table Search procedures. In modern applications, model space is a key factor, and a major open question concerning this area is to assess to what extent one can enjoy the speeding up of Binary Searches achieved by Learned Indexes while using constant or nearly constant-space mod…
Some Experiments in Supervised Pattern Recognition with Incomplete Training Samples
2002
This paper presents some ideas about automatic procedures to implement a system with the capability of detecting patterns arising from classes not represented in the training sample. The procedure aims at incorporating automatically to the training sample the necessary information about the new class for correctly recognizing patterns from this class in future classification tasks. The Nearest Neighbor rule is employed as the central classifier and several techniques are added to cope with the peril of incorporating noisy data to the training sample. Experimental results with real data confirm the benefits of the proposed procedure.
Towards Efficient Teacher Assisted Assignment Marking Using Ranking Metrics
2017
This paper describes a tool with supporting methodology for efficient teacher assisted marking of open assignments based on student answer ranking metrics. It includes a methodology for how to design tasks for markability. This improves marking efficienty and reduces cognitive strain for the teacher during marking, and also allows for easily giving feedback to students on common pitfalls and misconceptions to improve both the learning outcome for the students as well as the teacher’s productivity by reducing the time needed for marking open assignments. An advantage with the method is that it is language agnostic as well as generally being agnostic to the discipline of the course being asse…
Ontology-Guided Approach to Feature-Based Opinion Mining
2011
The boom of the Social Web has had a tremendous impact on a number of different research topics. In particular, the possibility to extract various kinds of added-value, informational elements from users' opinions has attracted researchers from the information retrieval and computational linguistics fields. However, current approaches to socalled opinion mining suffer from a series of drawbacks. In this paper we propose an innovative methodology for opinion mining that brings together traditional natural language processing techniques with sentimental analysis processes and Semantic Web technologies. The main goals of this methodology is to improve feature-based opinion mining by employing o…
Learning high-level manipulative tasks through imitation
2006
This paper presents ConSCIS, Conceptual Space based Cognitive Imitation System, which tightly links low-level data processing with knowledge representation in the context of robot imitation. Our focus is on the program-level imitation: we are interested in the final effects of actions on objects, and not on the particular kinematic or dynamic properties of the motion. The same architecture is used both to analyze and represent the task to be imitated, and to perform the imitation by generalizing in novel and different circumstances. The implemented experimental scenario is a two dimensional world populated with various objects in which observation/imitation takes place. To validate our appr…
Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks
2016
The continuously growing framework of information dynamics encompasses a set of tools, rooted in information theory and statistical physics, which allow to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of complex networks. Building on the most recent developments in this field, this work designs a complete approach to dissect the information carried by the target of a network of multiple interacting systems into the new information produced by the system, the information stored in the system, and the information transferred to it from the other systems; information storage and transfer are then further decomposed into amou…