The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.
The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.
These slides include many inappropriate graphs. If you want to tell the summary of the data correctly, you should avoid to use graphs in this presentation. They can mislead those who view them.
In English, the title of presentaion is "24 slides including graphs that should not be absolutely drawn".
This document discusses methods for automated machine learning (AutoML) and optimization of hyperparameters. It focuses on accelerating the Nelder-Mead method for hyperparameter optimization using predictive parallel evaluation. Specifically, it proposes using a Gaussian process to model the objective function and perform predictive evaluations in parallel to reduce the number of actual function evaluations needed by the Nelder-Mead method. The results show this approach reduces evaluations by 49-63% compared to baseline methods.
These slides include many inappropriate graphs. If you want to tell the summary of the data correctly, you should avoid to use graphs in this presentation. They can mislead those who view them.
In English, the title of presentaion is "24 slides including graphs that should not be absolutely drawn".
This document discusses methods for automated machine learning (AutoML) and optimization of hyperparameters. It focuses on accelerating the Nelder-Mead method for hyperparameter optimization using predictive parallel evaluation. Specifically, it proposes using a Gaussian process to model the objective function and perform predictive evaluations in parallel to reduce the number of actual function evaluations needed by the Nelder-Mead method. The results show this approach reduces evaluations by 49-63% compared to baseline methods.
The document provides instructions for installing Quantum GIS (QGIS) on Windows, Mac, and Linux systems. For Windows users, it recommends downloading the OSGeo4W installer which contains QGIS and its dependencies. For Mac users, it instructs to install GDAL and GSL frameworks before downloading and installing QGIS 1.8.0 from a specific website. For Linux users, it lists commands to install QGIS using the system's package manager. It concludes by verifying QGIS is working properly after installation.
Hiroaki Sengoku gave a presentation on open source GIS. He began with an introduction and overview of open source GIS. Some major open source GIS programs discussed included QGIS, GRASS, PostGIS, and GDAL/OGR. Sengoku then covered how to learn open source GIS through scripting languages like Python and provided an example using PyQt. Finally, he presented a case study on estimating fire spreading using open source GIS and data from Zenrin Maps.
GIS future prospects pioneered by microgeodata usageHiroaki Sengoku
The document discusses microgeodata (MGD) and its potential to advance GIS. It introduces MGD as vector data at a finer scale than typically used, such as individual buildings and road segments. The document outlines challenges to using MGD like cost and privacy, but notes many sources are now freely available for research. Examples are given of analysis that can be done with MGD, such as estimating populations and visualizing people flows. The document advocates for more MGD research and discusses the speaker's work analyzing urban spaces using MGD as a "doctor examines a patient" based on available spatial data.
91. 5.6 Pythonで動かしてみよう: 到達点の頂点抽出
到達距離の設定
49 #Choosing upper bound vertexes
検索距離を指定します。
50 upperBound = [] ここでは「1000m」と設定
51 angle_dic = {} しています
52 r = 1000.0 #distance
53 i=0
到達範囲の頂点の抽出
55 while i < len(cost):
56 if cost[ i ] > r and tree[ i ] != -1:
57 outVertexId = graph.arc( tree [ i ] ).outVertex()
58 if cost[ outVertexId ] < r:
59
60 ~ 中略 ~
65 i=i+1
91
92. 5.7 Pythonで動かしてみよう: 到達圏ポリゴン作成
到達圏ポリゴン配列のソート
67 geomPolygon = []
68 for key, value in sorted(angle_dic.items()):
69 geomPolygon.append(value)
到達圏ポリゴンのジオメトリテーブルの作成
71 #Create Polygon about areas of the availability
72 Polygonset = [geomPolygon]
73 gPolygon = QgsGeometry.fromPolygon(Polygonset)
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