Detection and Tracking of Liquids with Fully Convolutional Networks
... It is clear from the figure that all three networks detect the liquid at least to some degree. The single frame CNN is less accurate at a pixel level, but it is still able to broadly detect the presence and general vicinity of liquid. As expected, the multi-frame CNN is much more precise than the si ...
... It is clear from the figure that all three networks detect the liquid at least to some degree. The single frame CNN is less accurate at a pixel level, but it is still able to broadly detect the presence and general vicinity of liquid. As expected, the multi-frame CNN is much more precise than the si ...
BACHELOR OF SCIENCE PROGRAM IN STATISTICS BACHELOR
... Statistical techniques in psychological research; correlational technique; experimental designs between and / or within subjects variations ; factorial design ; single subject design ; simple and multiple linear regression and correlation analysis ; other coefficients of correlation; analysis of cov ...
... Statistical techniques in psychological research; correlational technique; experimental designs between and / or within subjects variations ; factorial design ; single subject design ; simple and multiple linear regression and correlation analysis ; other coefficients of correlation; analysis of cov ...
Encoding and decoding in fMRI
... of the brain. Most current understanding has been achieved by analyzing fMRI data from the mirror perspectives of encoding and decoding. When analyzing data from the encoding perspective, one attempts to understand how activity varies when there is concurrent variation in the world. When analyzing d ...
... of the brain. Most current understanding has been achieved by analyzing fMRI data from the mirror perspectives of encoding and decoding. When analyzing data from the encoding perspective, one attempts to understand how activity varies when there is concurrent variation in the world. When analyzing d ...
Dr. Eick`s Introduction to AI
... • Relies on two-valued logic • Mostly uses a symbolic (non-numerical knowledge representation framework) Soft Computing (e.g. Fuzzy Logic, Belief Networks,..): • Tolerance for uncertainty and imprecision • Uses weights, probabilities, possibilities • Strongly relies on numeric approximation and inte ...
... • Relies on two-valued logic • Mostly uses a symbolic (non-numerical knowledge representation framework) Soft Computing (e.g. Fuzzy Logic, Belief Networks,..): • Tolerance for uncertainty and imprecision • Uses weights, probabilities, possibilities • Strongly relies on numeric approximation and inte ...
rene-witte.net - Semantic Scholar
... Our Fuzzy Believer system models a human newspaper reader who develops his own point of view for current events described in newspaper articles. More specifically, we only rely on information stated within the grammatical construct of reported speech. This allows a clear assignment of statements to ...
... Our Fuzzy Believer system models a human newspaper reader who develops his own point of view for current events described in newspaper articles. More specifically, we only rely on information stated within the grammatical construct of reported speech. This allows a clear assignment of statements to ...
What is Text Analytics?
... categorization, entities, metadata - > present to author – Cognitive task is simple -> react to a suggestion instead of select from head or a complex taxonomy – Feedback – if author overrides -> suggestion for new category – Facets – Requires a lot of Metadata - Entity Extraction feeds facets ...
... categorization, entities, metadata - > present to author – Cognitive task is simple -> react to a suggestion instead of select from head or a complex taxonomy – Feedback – if author overrides -> suggestion for new category – Facets – Requires a lot of Metadata - Entity Extraction feeds facets ...
Using Rewards for Belief State Updates in Partially Observable
... table. We note that in most POMDP examples, the number of possible immediate rewards satisfies this assumption, and is often very small. However, if this assumption is not satisfied, e.g. if reward are continuous, a conditional probability distribution over rewards can still be specified in some par ...
... table. We note that in most POMDP examples, the number of possible immediate rewards satisfies this assumption, and is often very small. However, if this assumption is not satisfied, e.g. if reward are continuous, a conditional probability distribution over rewards can still be specified in some par ...
Reports on the 2015 AAAI Workshop Series
... The formal task of algorithm configuration consists of determining a configuration c of a configuration space C from an algorithm A on an instance set I by optimizing a given performance metric m : C x I ? over all instances I. In contrast to the area of continuous black-box optimization (tackled, ...
... The formal task of algorithm configuration consists of determining a configuration c of a configuration space C from an algorithm A on an instance set I by optimizing a given performance metric m : C x I ? over all instances I. In contrast to the area of continuous black-box optimization (tackled, ...
Evaluating Analytical Data
... specific conclusion about the mass of any other penny (although we might conclude that all pennies weigh at least 3 g). We can, however, characterize this data by reporting the spread of individual measurements around a central value. 4A.1 ...
... specific conclusion about the mass of any other penny (although we might conclude that all pennies weigh at least 3 g). We can, however, characterize this data by reporting the spread of individual measurements around a central value. 4A.1 ...
ICT619 Intelligent Systems
... May be an irregular user such as bank customer Needs and characteristics of both categories of users need to be taken into account, particularly for the user interface design. The user must feel confident and happy with the system So a usability evaluation should be conducted with these peop ...
... May be an irregular user such as bank customer Needs and characteristics of both categories of users need to be taken into account, particularly for the user interface design. The user must feel confident and happy with the system So a usability evaluation should be conducted with these peop ...
Role of Expert Systems in Construction Roboticsl
... IF input is available THEN compute results lF results are computed THEN output results A process such as compute results is further subdivided into thousands of more detailed rules. As illustrated, the rules are intertwined; the action result of one rule becomes the premise of another rule. Traditio ...
... IF input is available THEN compute results lF results are computed THEN output results A process such as compute results is further subdivided into thousands of more detailed rules. As illustrated, the rules are intertwined; the action result of one rule becomes the premise of another rule. Traditio ...
Time series
A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).