標(biāo)題: Titlebook: Making the Most of Fieldwork Education; A Practical Approach Auldeen Alsop,Susan Ryan Book 1996 Auldeen Alsop and Susan Ryan 1996 assessmen [打印本頁] 作者: ACRO 時(shí)間: 2025-3-21 19:52
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書目名稱Making the Most of Fieldwork Education讀者反饋
書目名稱Making the Most of Fieldwork Education讀者反饋學(xué)科排名
作者: 蜈蚣 時(shí)間: 2025-3-21 21:35 作者: CUR 時(shí)間: 2025-3-22 02:20 作者: heart-murmur 時(shí)間: 2025-3-22 05:51
s opportunity to inte- grate the art, science and ethical practice of Occupational Therapy. The therapeutic milieu is also the most appropriate but difficult setting in which to judge ‘compe- tence to practise‘. Yet fieldwork education has not received the attention, resources, research and status i作者: 召集 時(shí)間: 2025-3-22 09:31
Book 1996ity to inte- grate the art, science and ethical practice of Occupational Therapy. The therapeutic milieu is also the most appropriate but difficult setting in which to judge ‘compe- tence to practise‘. Yet fieldwork education has not received the attention, resources, research and status it deserves作者: Campaign 時(shí)間: 2025-3-22 14:59
classifiers. The underlying methodology is to derive measures or score functions from generative models. The derived score functions, spanning the so-called score space, provide features of a fixed dimension for discriminative classification. In this paper, we propose a simple yet effective score s作者: debase 時(shí)間: 2025-3-22 17:56 作者: 悠然 時(shí)間: 2025-3-23 00:50
Auldeen Alsop,Susan Ryans paper, we present a novel stochastic method – Orthant Based Proximal Stochastic Gradient Method (OBProx-SG) – to solve perhaps the most popular instance, ., the .-regularized problem. The OBProx-SG method contains two steps: (i) a proximal stochastic gradient step to predict a support cover of the作者: 壟斷 時(shí)間: 2025-3-23 02:35 作者: 他去就結(jié)束 時(shí)間: 2025-3-23 06:58 作者: 貪婪地吃 時(shí)間: 2025-3-23 13:04
Auldeen Alsop,Susan Ryani) a simple convex term, and (iii) a concave and continuous term. First, by extending randomized CD to nonsmooth nonconvex settings, we develop a coordinate subgradient method that randomly updates block-coordinate variables by using block composite subgradient mapping. This method converges asympto作者: prosthesis 時(shí)間: 2025-3-23 15:08
Auldeen Alsop,Susan Ryansecond-order stationary point (. approximate local minimum) and thus escaping saddle points are sufficient for such functions to obtain a classifier with good generalization performance. Existing algorithms for escaping saddle points, however, all fail to take into consideration a critical issue in 作者: 使虛弱 時(shí)間: 2025-3-23 20:23 作者: 乳汁 時(shí)間: 2025-3-24 02:03
companies. An important question is the learning of models upon . (patients, customers) rather than the transactions, especially when these models are subjected to drift..We address this problem by combining advances of online clustering on multivariate data with the trajectory mining paradigm. We m作者: 消極詞匯 時(shí)間: 2025-3-24 06:01
Auldeen Alsop,Susan Ryan (CL) or a lower-bound surrogate of the CL. One training procedure is based on the extended Baum-Welch (EBW) algorithm. Similarly, the remaining two approaches iteratively optimize the parameters (initialized to ML) with a 2-step algorithm. In the first step, either the class posterior probabilities作者: 剛開始 時(shí)間: 2025-3-24 09:23
Auldeen Alsop,Susan Ryanledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019..The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. ..The contributions were organized in topical sectio作者: 收藏品 時(shí)間: 2025-3-24 12:24 作者: nocturnal 時(shí)間: 2025-3-24 15:26 作者: 發(fā)現(xiàn) 時(shí)間: 2025-3-24 22:36
Auldeen Alsop,Susan Ryanovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019..The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. ..The contributions were organized in topical sections named a作者: Distribution 時(shí)間: 2025-3-25 00:36 作者: 增長 時(shí)間: 2025-3-25 06:17
Auldeen Alsop,Susan Ryanhese into . class probabilities, supporting cost-optimal decision making. Isotonic calibration is the standard non-parametric calibration method for binary classifiers, and it can be shown to yield the most likely monotonic calibration map on the given data, where monotonicity means that instances w作者: discord 時(shí)間: 2025-3-25 09:57
Auldeen Alsop,Susan Ryanwith minimal programming effort. This is especially true for machine learning problems whose objective function is nicely separable across individual data points, such as classification and regression. In contrast, statistical learning tasks involving pairs (or more generally tuples) of data points—作者: 商店街 時(shí)間: 2025-3-25 12:14 作者: 我要威脅 時(shí)間: 2025-3-25 16:11 作者: 平淡而無味 時(shí)間: 2025-3-25 20:28
Auldeen Alsop,Susan Ryanle to provide the user with truly informative and useful views of the data. In our recently introduced framework for human-guided data exploration (Puolam?ki et al. [.]), both the user’s knowledge and objectives are modelled as distributions over data, parametrised by tile constraints. This makes it作者: 激怒 時(shí)間: 2025-3-26 03:26
erative model with an extra posterior imposed over its hidden variables. Experimental evaluation of this approach over two generative models shows that performance of the score space approach coupled with the proposed discriminative learning method is competitive with state-of-the-art classification作者: 水汽 時(shí)間: 2025-3-26 05:17 作者: lanugo 時(shí)間: 2025-3-26 09:20
Auldeen Alsop,Susan Ryanect of sparsity exploration and objective values. Moreover, the experiments on non-convex deep neural networks, ., MobileNetV1 and ResNet18, further demonstrate its superiority by generating the solutions of much higher sparsity without sacrificing generalization accuracy, which further implies that作者: Peak-Bone-Mass 時(shí)間: 2025-3-26 14:06 作者: Nefarious 時(shí)間: 2025-3-26 17:46 作者: 供過于求 時(shí)間: 2025-3-26 21:38 作者: 考古學(xué) 時(shí)間: 2025-3-27 04:10
Auldeen Alsop,Susan Ryanevious result on this problem is mainly of theoretical importance and has several issues (. high sample complexity and non-scalable) which hinder its applicability, especially, in big data. To deal with these issues, we propose in this paper a new method called Differentially Private Trust Region, a作者: Brain-Waves 時(shí)間: 2025-3-27 06:45 作者: 轉(zhuǎn)換 時(shí)間: 2025-3-27 13:05
n Mixture Models. We study (i) an EM-based approach that clusters these trajectories incrementally as a reference method that has access to all the data for learning, and propose (ii) an online algorithm based on a Kalman filter that efficiently tracks the trajectories of Gaussian clusters. We show 作者: orthodox 時(shí)間: 2025-3-27 15:03 作者: Enliven 時(shí)間: 2025-3-27 19:25
Auldeen Alsop,Susan RyanWe present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.
