Okay. In the last video, we spoke among other things about classification of supervised learning algorithms into probabilistic and non-probabilistic models. For probabilistic models, we distinguished between generative and discriminative probabilistic models. We also said that some non-probabilistic models can be equivalently formulated as probabilistic ones. For example, linear regression is equivalent to a discriminative Gaussian model. Now, let us talk about probabilistic classification models in finance. Classification models are obtained from probabilistic framework if the output variable Y is not a continuous variable, but rather a discrete label. This label has values CK, where k is the number of different classes. Note that the sum of probabilities over all classes should be one, obviously, so we get this relation here. Let us now consider a special case of classification one K equals two. So, in this case, we have only two states. We can call them plus and minus or zero and one, or ...

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I error). Two widely used statistical tests are shown to have high probability of type I error in certain situations and should never be used: a test for the difference of two proportions and a paired-differences t test based on taking several random train-test splits. A third test, a paired-differences t test based on 10-fold cross-validation, exhibits somewhat elevated probability of type I error. A fourth test, McNemars test, is shown to have low type I error. The fifth test is a new test, 5 × 2 cv, based on five iterations of twofold cross-validation. Experiments show that this test also has acceptable type I error. The article also

Calibration can be assessed using a calibration plot (also called a reliability diagram).[3][5] A calibration plot shows the proportion of items in each class for bands of predicted probability or score (such as a distorted probability distribution or the signed distance to the hyperplane in a support vector machine). Deviations from the identity function indicate a poorly-calibrated classifier for which the predicted probabilities or scores can not be used as probabilities. In this case one can use a method to turn these scores into properly calibrated class membership probabilities. For the binary case, a common approach is to apply Platt scaling, which learns a logistic regression model on the scores.[6] An alternative method using isotonic regression[7] is generally superior to Platts method when sufficient training data is available.[3] In the multiclass case, one can use a reduction to binary tasks, followed by univariate calibration with an algorithm as described above and further ...

04/05/19 - Unstructured data from diverse sources, such as social media and aerial imagery, can provide valuable up-to-date information for i...

Traditionally, machine learning has focussed on induction of classification and prediction rules. More recently, non-predictive or descriptive induction is gaining substantial interest of machine learning researchers. Two major trends in descriptive induction are association rule learning and subgroup discovery. In this seminar we present our recent work in descriptive induction. We also argue that accuracy is not always an appropriate evaluation measure in the descriptive induction framework, and propose quality measures designed for subgroup evaluation in ROC space. After a brief presentation of the APRIORI-C and SD-algorithm, we give a detailed presentation of the CN2-SD algorithm, which includes a new -- weighted -- covering algorithm, a new search heuristic (weighted relative accuracy), probabilistic classification of instances, and a new measure for evaluating the results of subgroup discovery (area under ROC curve). The presented work was done in collaboration with V. Jovanoski (APRIORI-C), D

A neural network and method for pipeline operation within a neural network which permits rapid classification of input vectors provided thereto is disclosed. In a training mode, a plurality of training input features are presented to the neural network and distances between the plurality of training features and a plurality of prototype weight values are concurrently computed. In response to an indication of a last training epoch count values for each of the prototype weight values are stored in a memory to thereby allow the neural network to operate in a probabilistic classification mode.

The risk of local recurrence for breast cancers is strongly correlated with the presence of a tumor within 1 to 2 mm of the surgical margin on the excised specimen. Previous experimental and theoretical results suggest that spatially offset Raman spectroscopy (SORS) holds much promise for intraoperative margin analysis. Based on simulation predictions for signal-to-noise ratio differences among varying spatial offsets, a SORS probe with multiple source-detector offsets was designed and tested. It was then employed to acquire spectra from 35 frozen-thawed breast tissue samples in vitro. Spectra from each detector ring were averaged to create a composite spectrum with biochemical information covering the entire range from the tissue surface to ∼2 mm below the surface, and a probabilistic classification scheme was used to classify these composite spectra as negative or positive margins. This discrimination was performed with 95% sensitivity and 100% specificity, or with 100% positive predictive value

Here you can investigate further how the CO2-emission level (CO2/km), modes of transport, gender, and age varies among the various responses to the questionnaire. Tick an answer alternative below and you will get a result summary for participants who selected this response alternative. Explanation of terms and units ...

TY - JOUR. T1 - Differential contributions of worry, anxiety, and obsessive compulsive symptoms to ERN amplitudes in response monitoring and reinforcement learning tasks. AU - Zambrano-Vazquez, Laura. AU - Allen, John J.B.. PY - 2014/8. Y1 - 2014/8. N2 - Obsessive-Compulsive Disorder (OCD) is characterized by intrusive thoughts (i.e. obsessions) and future-oriented worrisome cognitions that are associated with behavioral ritualistic compensations (i.e. compulsions) and anxious arousal. Research has found an enhanced error-related negativity (ERN) among those with OCD in choice response tasks such as the flankers task, but not in probabilistic learning tasks. To date, research has not directly investigated whether the ERN effect observed in individuals with OCD is specific to the central features of OCD (obsessions and compulsions), or is related more closely to the worry or anxiety observed in this disorder. This study compared groups with relatively pure symptom profiles on OC, worry, and ...

PubMed Central Canada (PMC Canada) provides free access to a stable and permanent online digital archive of full-text, peer-reviewed health and life sciences research publications. It builds on PubMed Central (PMC), the U.S. National Institutes of Health (NIH) free digital archive of biomedical and life sciences journal literature and is a member of the broader PMC International (PMCI) network of e-repositories.

Jonathan W Kanen, Frederique E Arntz, Robyn Yellowlees, Rudolf N Cardinal, Annabel Price, David M Christmas, Barbara J Sahakian, Annemieke M Apergis-Schoute, Trevor W Robbins ...

A learning task in which different responses are associated with differing intermittent reinforcement schedules as described in Pizzagalli et al (2005). Go back to Probabilistic Reward Task page ...

The study of perceptual decisions has been developed as a substitute for investigating more complex multiple attribute decisions. However, little attention has been paid to the similarity of results between the two literatures. Four separate behavioral experiments and a secondary trial- by-trial analysis investigated the sensitivity of perceptual decisions. Results were compared to both previous perceptual decision research and that of multiple attribute decisions in an effort to bridge the divide. The first experiment examined the effect of increasing the similarity of available response alternatives on accuracy and reaction time. The results suggest that high levels of similarity can begin to degrade the decision process by lowering accuracy and slowing reaction time; however these changes may be dependent on the extent to which the alternatives use overlapping neuronal pools. The second experiment examined the effect of increasing the number of response alternatives available for a single ...

There is increasing evidence that gene location and surrounding genes influence the functionality of genes in the eukaryotic genome. Knowing the Gene Ontology Slim terms associated with a gene gives us insight into a genes functionality by informing us how its gene product behaves in a cellular context using three different ontologies: molecular function, biological process, and cellular component. In this study, we analyzed if we could classify a gene in Saccharomyces cerevisiae to its correct Gene Ontology Slim term using information about its location in the genome and information from its nearest-neighbouring genes using classification learning. We performed experiments to establish that the MultiBoostAB algorithm using the J48 classifier could correctly classify Gene Ontology Slim terms of a gene given information regarding the genes location and information from its nearest-neighbouring genes for training. Different neighbourhood sizes were examined to determine how many nearest neighbours

Mareschal, Denis and Johnson, S.P. (2002) Learning to perceive object unity: a connectionist account. Developmental Science 5 (2), pp. 151-172. ISSN 1363-755x. Mareschal, Denis and Quinn, P.C. and French, R.M. (2002) Asymmetric interference in 3- to 4-month-olds sequential category learning. Cognitive Science 26 (3), pp. 377-389. ISSN 0364-0213. Moore, S.C. and Oaksford, Michael (2002) Emotional cognition: an introduction. In: Moore, S.C. and Oaksford, Michael (eds.) Emotional Cognition. Advances in Consciousness Research 44. Amsterdam, The Netherlands: John Benjamins Publishing, pp. 1-8. ISBN 9789027251688. Moore, S.C. and Oaksford, Michael (2002) An informational value for mood: negative mood biases attention to global information in a probabilistic classification task. In: Moore, S.C. and Oaksford, Michael (eds.) Emotional Cognition. Advances in Consciousness Research 44. Amsterdam, The Netherlands: John Benjamins Publishing, pp. 221-243. ISBN 9789027251688. ...

Predicting future reward is paramount to performing an optimal action. Although a number of brain areas are known to encode such predictions, a detailed account of how the associated representations evolve over time is lacking. Here, we address this question using human magnetoencephalography (MEG) and multivariate analyses of instantaneous activity in reconstructed sources. We overtrained participants on a simple instrumental reward learning task where geometric cues predicted a distribution of possible rewards, from which a sample was revealed 2000 ms later. We show that predicted mean reward (i.e., expected value), and predicted reward variability (i.e., economic risk), are encoded distinctly. Early on, representations of mean reward are seen in parietal and visual areas, and later in frontal regions with orbitofrontal cortex emerging last. Strikingly, an encoding of reward variability emerges simultaneously in parietal/sensory and frontal sources and later than mean reward encoding. An orbitofrontal

Do you focus on the statistics of your poker hand? Or do you just know what hands you should and should not play and not pay attention to

This thesis is concerned with the problem of how people learn to use uncer-tain information for making judgments. The general framework for the thesis is Social Judgment Theory (SJT). First the S3T paradigm, and some research conducted within the paradigm, is briefly described, and a series of four empirical studies is summarized. The studies are concerned with two factors that have been found to have great effect on subjects achievement in cue probability learning (CPL) tasks: task predictability, and the form of the function relating cue and criterion. The effects of these two factors were studied in experiments employing cue-probability learning tasks. The studies concerned with task predictability addressed the following questions (a) Do subjects understand the probabilistic nature of CPL-tasks? (b) Are subjects able to detect that a random task is, in fact, random, a study undertaken to test an aspect of Seligmans theory of helplessness. This was also an attempt to bring emotional factors ...

Check out this free ebook covering the elements of statistical learning, appropriately titled The Elements of Statistical Learning.

Immediate treatment protocols have been established since years in dentistry and in Straumann® - ultimately the clinicians decision following their preference and level of confidence. With Straumann® solutions like Bone Level Tapered Implants (BLT) for immediacy we provide holistic concepts that deliver primary stability with advanced healing capabilities due to SLActive® in combination with a fully integrated digital workflow - advantages for both clinician and patient ...

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BACKGROUND: Recent studies implicate individual differences in regulatory focus as contributing to self-regulatory dysfunction, particularly not responding to positive outcomes. How such individual differences emerge, however, is unclear. We conducted a proof-of-concept study to examine the moderating effects of genetically driven variation in dopamine signaling, a key modulator of neural reward circuits, on the association between regulatory focus and reward cue responsiveness. METHOD: Healthy Caucasians (N=59) completed a measure of chronic regulatory focus and a probabilistic reward task. A common functional genetic polymorphism impacting prefrontal dopamine signaling (COMT rs4680) was evaluated. RESULTS: Response bias, the participants propensity to modulate behavior as a function of reward, was predicted by an interaction of regulatory focus and COMT genotype. Specifically, self-perceived success at achieving promotion goals predicted total response bias, but only for individuals with the ...

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work. ...

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and mark

Use message dialogs to present blocking questions that require the users input. A blocking question is a question where the application cannot make a choice on the users behalf, and cannot continue to fulfill its value proposition to the user. A blocking question should present clear choices to the user. It is not a question that can be ignored or postponed.. ...

To make this case about the influence of our language choices on our efforts to engage in responsible research and innovation, I will conduct a metaphor

So my husband is re-enlisting in the Marines, got his station orders today, and its Japan. He put the choice on me on whether to go or not. Ive never been ...

This article is about mathematical term. For the novel, see Probability Space (novel). In probability theory, a probability space or a probability triple is a mathematical construct that models a real world process (or experiment ) consisting of…

海词词典，最权威的学习词典，专业出版probability concept是什么意思，probability concept的用法，probability concept翻译和读音等详细讲解。海词词典：学习变容易，记忆很深刻。

The probability of being left-handed is 0.12. If we sample 25 people at random, what is the probability that exactly 2 people are left-handed? how do

Definitions of the important terms you need to know about in order to understand Probability, including Event , Complementary Events , Odds , Outcomes , Probability

Hello, In the family with geometric distribution population average is 3 . The probability what percentage families exactly has 3 population? (Average

το κείμενο με τίτλο uncertaintysubjective probability σχετίζετε με Ηλεκτρονική - Συσκευές

Identifying mechanisms through which individual differences in reward learning emerge offers an opportunity to understand both a fundamental form of adaptive responding as well as etiological pathways through which aberrant reward learning may contribute to maladaptive behaviors and psychopathology. One candidate mechanism through which individual differences in reward learning may emerge is variability in dopaminergic reinforcement signaling. A common functional polymorphism within the catechol-O-methyl transferase gene (COMT; rs4680, Val158Met) has been linked to reward learning, where homozygosity for the Met allele (linked to heightened prefrontal dopamine function and decreased dopamine synthesis in the midbrain) has been associated with relatively increased reward learning. Here, we used a probabilistic reward learning task to asses response bias, a behavioral form of reward learning, across three separate samples that were combined for analyses (age: 21.80 ± 3.95; n = 392; 268 female; ...

The present study found that damage to OFC, but not to other areas within PFC, resulted in impaired performance on a probabilistic reversal learning task. This is consistent with two previous neuropsychological studies that reported impaired performance of a complex, gambling-like reversal learning task after OFC damage (Berlin et al., 2004; Hornak et al., 2004). The large cohort studied here allowed VLSM analysis to be applied to much of the PFC. We were able to confirm a regionally specific contribution of OFC, with the effect mainly driven by voxels in bilateral posteromedial OFC and to a lesser extent right lateral OFC, and to reject a critical role for other regions within PFC, notably including dACC (at least of the same effect size and within the anatomical constraints of our sample) in flexible reinforcement learning in a probabilistic environment.. Contrary to the performance of such patients on a simple, deterministic reversal learning task (Fellows and Farah, 2003), the impairment of ...

Mô tả: A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS) lesions in 3D Magnetic Resonance (MR) images. It builds on a discriminative random decision forest framework to provide a voxel-wise probabilistic classification of the volume. The method uses multi-channel MR intensities (T1, T2, and FLAIR), knowledge on tissue classes and long-range spatial context to discriminate lesions from background. A symmetry feature is introduced accounting for the fact that some MS lesions tend to develop in an asymmetric way. Quantitative evaluation of the proposed methods is carried out on publicly available labeled cases from the MICCAI MS Lesion Segmentation Challenge 2008 dataset. When tested on the same data, the presented method compares favorably to all earlier methods. In an a posteriori analysis, we show how selected features during classification can be ranked according to their discriminative power and reveal the most important ones. ►... ...

Prediction errors are critical for associative learning [1, 2]. Transient changes in dopamine neuron activity correlate with positive and negative reward prediction errors and can mimic their effects [3-15]. However, although causal studies show that dopamine transients of 1-2 s are sufficient to drive learning about reward, these studies do not address whether they are necessary (but see [11]). Further, the precise nature of this signal is not yet fully established. Although it has been equated with the cached-value error signal proposed to support model-free reinforcement learning, cached-value errors are typically confounded with errors in the prediction of reward features [16 ...

is the standard deviation, and N is the number of data points. Note that in computing the skewness, the s is computed with N in the denominator rather than N - 1. The above formula for skewness is referred to as the Fisher-Pearson coefficient of skewness. Many software programs actually compute the adjusted Fisher-Pearson coefficient of skewness \[ G_{1} = \frac{\sqrt{N(N-1)}}{N-2} \frac{\sum_{i=1}^{N}(Y_{i} - \bar{Y})^{3}/N} {s^{3}} \] This is an adjustment for sample size. The adjustment approaches 1 as N gets large. For reference, the adjustment factor is 1.49 for N = 5, 1.19 for N = 10, 1.08 for N = 20, 1.05 for N = 30, and 1.02 for N = 100. The skewness for a normal distribution is zero, and any symmetric data should have a skewness near zero. Negative values for the skewness indicate data that are skewed left and positive values for the skewness indicate data that are skewed right. By skewed left, we mean that the left tail is long relative to the right tail. Similarly, skewed right means ...

Part Of: Neuroeconomics sequence Content Summary: 8min reading time, 800 words Reward Prediction Error An efficient way to learn about the world and its effect on the organism is by utilizing a reward prediction error (RPE) signal, defined as: $latex \Delta_t = \left[ r_t(A) + \gamma \sum P(s|s)V_{t+1}(s) \right] - V_t(s)$ The RPE is derived from the Bellman equation, and…

Ahead of his Party Conference, which begins tomorrow, Liberal Democrat Leader Ming Campbell, has called for the public to be given a real choice on the European Union. Ming said:

The skewness formula is used to measure the symmetry of a distribution around its mean. Find out more about what skewness is online at Quality America!

OK, so this story is about weeds and weedkillers, neither of which is ever the hero of a story, but stay with me for a second: It's also about plants

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Those who are self-employed are wise to make strong choices on how best to save and protect their money in planning for retirement, experts say.

Know the impact of lifestyle choices on your risk of developing diabetes. Find tools and resources to reduce your risk, from fitness tips to healthy recipes.

01/08/08 - We give an overview of two approaches to probability theory where lower and upper probabilities, rather than probabilities, are us...

Probability is simply the likelihood of an event happening. It can be expressed in the following ways: 1. Equal Probability 2. Likely Probability 3. Unlikely Probability

Probability - Ask a question about Probability, start a discussion about Probability, share your opinion about Probability, or write an online review

Probability - Ask a question about Probability, start a discussion about Probability, share your opinion about Probability, or write an online review

What is probability? Describes how to interpret probability. Shows how to compute probability. Includes sample problems with solutions plus free, video lesson.

Having problems converting some probabilities into odds. Need to turn these probabilities into odds as 1 in X chance 0.192 0.01408 0.00128 0.000192 0.00000256 0.00000128 0.000000256

If a die rolled one time, classical probability would indicate that the probability of a two should be 1/6. If the die is rolled 60 times and comes up two only 9 times, does this suggest that the die is loaded ? Why or why.

Read more about the influence of probability in case of a forex trading system. Learn also the influence of different risk/reward ratios in case of a trading system.

The probability that an event will occur is fraction 2 over 3 . Which of these best describes the likelihood of the event occurring? Likely Cer...

a) On the first draw, there are `4` defectives in the box out of the `100` total items.. If we have already chosen one of the defectives on the first draw, then on the second draw, there will be `3` defectives left out of the `99` items in the box. The required probability is: `4/100 times 3/99 = 1/825 = 1.2121 times 10^-3`. (b) Both the first draw and the second draw have the same probability of getting a defective, i.e. `4` in `100`. The required probability is: `4/100 times 4/100 = 1/625 = 0.0016`. (c) We can either:. ...